Keywords: Perception systems, Robot control

Keywords: Control applications, Electric vehicles and intelligent transportation., Nonlinear systems
Abstract: This paper utilises feedback linearization to enable the linearization and state feedback stabilised control of an electric vehicle. Key features are centred around the development of a linearizing slip controller through input out feedback linearization. The key contribution is the hidden state stability being established through the stricter Lyapunov conditions for asymptotic stability of the zero hidden state dynamics. Braking performance guarantees are demonstrated through various detailed braking performance metrics such as drive current, distance performance, and braking speed analysis.

Keywords: Control applications, Energy management systems, Electric vehicles and intelligent transportation.
Abstract: For the electrified powertrain of vehicles, the high energy efficiency is achieved by management of the energy flow which provides necessary propulsion power under the constraint of driver's demand. Moreover, managing the energy flow between a group of electrified vehicles, charging/discharging the battery, is also an important issue to improve the energy efficiency from the view of global energy consumption. However, a common challenging issue is to known the demand previously before making optimal decision. This paper introduces an approach to predict a long-term demand based on the mean-field limitation of a large-scale population of agents. First, a brief review of the mean-field limitation method is given which focused on representation of the propagation of state distribution function. Then, two application examples are shortly introduced for using the mean-field limitation approach to represent the collective energy storage of a large-scale parking and to predict a driving route in the sense of average driving speed, respectively. With the representation, the electricity trading problem between the grid and the parking is formulated as an optimization problem with the battery storage constraint, and the long-term energy management problem is formulated as the predicted demand constraint optimization problem.

Keywords: Electric vehicles and intelligent transportation., Smart grid, Planning, scheduling and coordination
Abstract: In the V2G (Vehicle to Grid) interaction scenario, there is a learning process for obtaining optimal strategies due to the gradual rationality of electric vehicles and microgrids. This paper compares four evolutionary algorithms—replicator dynamics, reinforcement learning, belief learning, and experience-weighted attraction (EWA)—in V2G interactions according to different interaction scenarios. By constructing a V2G game model and performing simulations, we evaluate these algorithms in terms of information processing, learning speed, and equilibrium results. The EWA algorithm demonstrates superior efficiency and stability, making it a promising tool for V2G strategy optimization. This study provides insights into the application of learning algorithms in V2G games, offering guidance for future IoV (Internet of Vehicles) developments, especially in grid load management and energy dispatch.

Keywords: Intelligent systems, Mobile robotics, Electric vehicles and intelligent transportation.
Abstract: This research advances autonomous vehicle capabilities by developing novel trajectory planning and control techniques for lane changes using free-driving and vehicle-following modes. This study addresses the critical challenge of enhancing lane change precision to minimize accident risks on highways. A quintic polynomial is used for trajectory generation, and a PID controller is employed for trajectory tracking, integrating collision prevention strategies for safety assurance. The contributions of this work include the development of an efficient control scheme that adapts to dynamic traffic conditions and improves the robustness of lane-changing maneuvers. The proposed approach significantly enhances the accuracy and reliability of autonomous lane changes, providing a pathway for safer autonomous driving in complex highway scenarios.

Keywords: Object recognition, Vision for robots, Electric vehicles and intelligent transportation.
Abstract: Autonomous Driving (AD) systems are considered as the future of human mobility and transportation. Solving computer vision tasks such as image classification and object detection/segmentation, with high accuracy and low power/energy consumption, is highly needed to realize AD systems in real life. These requirements can potentially be satisfied by Spiking Neural Networks (SNNs). However, the state-of-the-art works in SNN-based AD systems still focus on proposing network models that can achieve high accuracy, and they have not systematically studied the roles of SNN parameters when used for learning event-based automotive data. Therefore, we still lack understanding of how to effectively develop SNN models for AD systems. Toward this, we propose a novel methodology to systematically study and analyze the impact of SNN parameters considering event-based automotive data, then leverage this analysis for enhancing SNN developments. To do this, we first explore different settings of SNN parameters that directly affect the learning mechanism (i.e., batch size, learning rate, neuron threshold potential, and weight decay), then analyze the accuracy results. Afterward, we propose techniques that jointly improve SNN accuracy and reduce training time. Experimental results show that our methodology can improve the SNN models for AD systems than the state-of-the-art, as it achieves higher accuracy (i.e., 86%) for the NCARS dataset, and it can also achieve iso-accuracy (i.e., ~85% with standard deviation less than 0.5%) while speeding up the training time by 1.9x. In this manner, our research work provides a set of guidelines for SNN parameter enhancements, thereby enabling the practical developments of SNN-based AD systems.

Keywords: Planning, scheduling and coordination, Electric vehicles and intelligent transportation., Intelligent systems
Abstract: Lane merging is one of the critical tasks for self-driving cars, and how to perform lane-merge maneuvers effectively and safely has become one of the important standards in measuring the capability of autonomous driving systems. However, due to the ambiguity in driving intentions and right-of-way issues, the lane merging process in autonomous driving remains deficient in terms of maintaining or ceding the right-of-way and attributing liability, which could result in protracted durations for merging and problems such as trajectory oscillation. Hence, we present a rule-compliance path planner (RCPP) for lane-merge scenarios, which initially employs the extended responsibility-sensitive safety (RSS) to elucidate the right-of-way, followed by the potential field-based sigmoid planner for path generation. In the simulation, we have validated the efficacy of the proposed algorithm. The algorithm demonstrated superior performance over previous approaches in aspects such as merging time (Saved 72.3%), path length (reduced 53.4%), and eliminating the trajectory oscillation.

Keywords: Localization, navigation and mapping, Nano-scale automation and assembly, Intelligent systems
Abstract: Detecting damage to bridges is essential for safety and financial reasons. We focus on inspections inside bridges and claim Unmanned Aerial Vehicles (UAVs) are ideally suited to assist inspections of hard-to-reach areas. Using UAVs in narrow indoor environments imposes constraints on the size of the UAV. While small UAVs are attractive for their agility and reduced risks associated with them, the need to carry cameras and sensors results in a lower bound on thrust and thus UAV and rotor size. Moreover, position and orientation control is particularly demanding inside bridges because satellite navigation is not available. To address these challenges, we implement a nano UAV that is capable of analyzing camera data for cracks with a machine learning model. UAV position is tracked with inertial measurement units, an optical flow, and a laser-based range sensor.

Keywords: Discrete event systems
Abstract: In this paper, we investigate property verification problems in partially-observed discrete-event systems (DES). Particularly, we are interested in verifying observational prop- erties that are related to the information-flow of the system. Observational properties considered here include diagnosabil- ity, predictability, detectability and opacity, which have drawn considerable attentions in the literature. However, in contrast to existing results, where different verification procedures are developed for different properties case-by-case, in this work, we provide a unified framework for verifying all these properties by reducing each of them as an instance of HyperLTL model checking.

Keywords: Discrete event systems, Cyber-physical systems, Planning, scheduling and coordination
Abstract: In this paper, we present a method for synthesizing optimal policies for stochastic systems under high-level mission specifications while maintaining information security. The stochastic systems are modeled as probabilistic labeling Markov decision processes (PLMDPs), and the high-level mission specifications are represented by linear temporal logic (LTL) formulas, which must be satisfied repeatedly, infinitely often. Meanwhile, an intruder is modeled as an observer who knows the exact structure of the system and passively monitors its behavior, aiming to infer the high-level mission specifications. Our objective is to synthesize an optimal policy that minimizes the mean payoff cost while preventing the intruder from definitively determining the given mission specifications. We begin by extending the PLMDP model by incorporating an observation function and the corresponding probability function, and we introduce the concept of LTL opacity for stochastic systems. Next, by proposing a new structure called the synchronous accepting maximally end component, we identify the subset of the accepting end components that satisfy the LTL opacity requirement. Finally, we develop a modified linear program with information security constraints to synthesize an optimal policy that ensures both formal correctness and LTL opacity.

Keywords: Discrete event systems, Planning, scheduling and coordination, multi-robot systems
Abstract: In various practical applications, collaborative inspection systems in which multiple agents work together to accomplish inspection tasks are becoming increasingly important for enhancing operational efficiency. This paper addresses the routing problem in multi-agent collaborative inspection systems, where certain inspection points require the simultaneous presence of multiple agents to perform inspection operations. A novel approach using max-plus algebra is presented to model the collaborative inspection process and it provides a foundation for research and applications in system control and scheduling optimization. The max-plus linear (MPL) model is then converted into a Mixed Integer Linear Programming (MILP) formulation to tackle the routing problem with complex constraints inherent in the collaborative scene. Experimental results validate that the proposed MPL model and MILP approach reduce inspection completion time and waiting time at collaborative points.

Keywords: Distributed optimization and MPC, Event-triggered and self-triggered control, Multi-agent systems
Abstract: This paper considers the distributed nonconvex optimization problem, where the goal is to minimize the average of local nonconvex cost functions through local information exchange. Firstly, we propose a distributed optimization algorithm that integrates the gradient tracking method with a dynamic event-triggered communication scheme, thereby reducing communication overhead. Secondly, we demonstrate that the algorithm linearly converges to the global optimum under the Polyak--Łojasiewicz condition, which indicates that every stationary point is a global minimizer. The numerical experiment is presented to validate the theoretical results and confirm the algorithm’s effectiveness.

Keywords: Distributed optimization and MPC, Intelligent automation
Abstract: This paper addresses the abstract dynamic programming (DP) in the online scenario, where the abstract DP mapping is time-varying, instead of static. In this case, optimal costs and policies at different time instants are not the same in general, and the problem amounts to tracking time-varying optimal costs and policies, which is of interest to many practical problems. It is thus necessary to analyze the performance of classical value iteration (VI) and policy iteration (PI) algorithms in the online case. In doing so, this paper develops and provides the theoretical analysis for several online algorithms, including approximate online VI, online PI, approximate online PI, online optimistic PI, and approximate online optimistic PI algorithms. It is proved that the tracking error bounds for all algorithms critically depend upon the largest difference between any two consecutive abstract mappings. Meanwhile, examples are presented to illustrate the theoretical results.

Keywords: Distributed optimization and MPC, Multi-agent systems
Abstract: In this paper, the problem of online distributed optimization with stochastic and nonconvex objective functions is studied by employing a multi-agent system. When making decisions, each agent only has access to a noisy gradient of its own objective function in the previous time and can only communicate with its immediate neighbors via a time-varying digraph. To handle this problem, an online distributed stochastic projectionfree algorithm is proposed. Of particular interest is that the dynamic regrets are employed to measure the performance of the online algorithm. Existing works on online distributed algorithms involving stochastic gradients only provide the sublinearity results of regrets in expectation. Different from them, we study the high probability bounds of the dynamic regrets, i.e., the sublinear bounds of dynamic regrets are characterized by the natural logarithm of the failure probability' s inverse. Under mild assumptions on the graph and objective functions, we prove that if the variations in both the objective function sequence and its gradient sequence grow within a certain rate, then the high probability bounds of the dynamic regrets grow sublinearly. Finally, a simulation example is carried out to demonstrate the effectiveness of our theoretical results.

Keywords: Localization, navigation and mapping, Visual servoing, Search, rescue and field robotics
Abstract: GPS is widely used for the localization of unmanned aerial vehicles due to its universal nature and high precision when combined with an IMU. However, a GPS relies on communication with satellites, and in some scenarios, this communication can be blocked rendering the method unusable. In these scenarios, an alternative localization method is needed as relying entirely on an IMU-based approach results in high degrees of drift.

A solution to this problem is using a camera, to track the takeoff position of the UAV, and utilizing the pin-hole model to estimate the distance to the takeoff position. The greatest strength of this solution is that the UAV can locate itself without relying on communication. However, there are several challenges associated with utilizing a single camera for localization, especially at long distances, as the noise quickly grows. This paper presents a solution to this problem and a real-life implementation displaying the viability of the method.

Keywords: Complex systems, Control applications
Abstract: As regards gas turbine engine control, how to achieve the optimal performance of engine within multiple control and physical constraints in real time is a sticky problem to be solved. A novel multivariable optimal control architecture is proposed to compensate engine into a pseudo-linear system by using the nonlinear dynamic inverse and the constrained optimization, and then linear feedback controller is used for control synthesis. At each sampling instant, a real-time linearized model of engine is applied to reduce computational complexity. The simulation results show the resultant control system of civil turbofan engine has good decoupling and tracking response.

Keywords: Vision for robots, Object recognition, Localization, navigation and mapping
Abstract: Fiducial markers play a common role in robotics, but their usability is constrained by the distance they can operate in. This limitation arises from the pixel count necessary for accurate detection. This paper presents a fiducial marker designed to be identifiable from just a few pixels. The proposed marker utilizes colors to improve the certainty of the detection while keeping the features of the marker as simple as possible. Experiments have been conducted to show the range in which the proposed marker can be used compared to the popular ArUco marker.

Keywords: Localization, navigation and mapping, multi-robot systems, Mobile robotics
Abstract: In autonomous robotics, a significant challenge involves devising robust solutions for Active Collaborative SLAM (AC-SLAM). This process requires multiple robots to cooperatively explore and map an unknown environment by intelligently coordinating their movements and sensor data acquisition. In this article, we present an efficient visual AC-SLAM method using aerial and ground robots for environment exploration and mapping. We propose an efficient frontiers filtering method that takes into account the common IoU map frontiers and reduces the frontiers for each robot. Additionally, we present an approach to guide robots to previously visited goal positions to promote loop closure and reduce SLAM uncertainty. The proposed method is implemented in ROS and evaluated through simulations on publicly available datasets and similar methods, achieving an accumulative average of 59% increase in area coverage.

Keywords: Localization, navigation and mapping, Building energy efficiency., Mobile robotics
Abstract: Optimizing Deep Learning-based Simultaneous Localization and Mapping (DL-SLAM) algorithms is essential for efficient implementation on resource-constrained embedded platforms, enabling real-time on-board computation in autonomous mobile robots. This paper presents SPAQ-DL-SLAM, a framework that strategically applies Structured Pruning and Quantization to the architecture of one of the state-of-the- art DL-SLAM algorithms, DROID-SLAM, for resource and energy-efficient optimization. Specifically, we perform structured pruning with fine-tuning based on layer-wise sensitivity analysis followed by 8-bit post-training static quantization(PTQ) on the deep learning modules within DROID-SLAM. Our SPAQ-DROID-SLAM model, optimized version of DROID- SLAM model using our SPAQ-DL-SLAM framework with 20% structured pruning and 8-bit PTQ, achieves an 18.9% reduction in FLOPs and a 79.8% reduction in overall model size compared to the DROID-SLAM model. Our evaluations on the TUM-RGBD benchmark shows that SPAQ-DROID- SLAM model surpasses the DROID-SLAM model by an average of 10.5% on absolute trajectory error (ATE) metric. Additionally, our results on the ETH3D SLAM training benchmark demonstrate enhanced generalization capabilities of the SPAQ-DROID-SLAM model, seen by a higher Area Under the Curve (AUC) score and success in 2 additional data sequences compared to the DROID-SLAM model. Despite these improvements, the model exhibits performance variance on the distinct Vicon Room sequences from the EuRoC dataset, which are captured at high angular velocities. This varying performance at some distinct scenarios suggests that designing DL-SLAM algorithms taking operating environments and tasks in consideration can achieve optimal performance and resource efficiency for deployment in resource-constrained embedded platforms.

Keywords: Localization, navigation and mapping, Distributed optimization and MPC, Robot control
Abstract: The paper describes a novel, publicly available toolbox for piecewise-affine (PWA) convex-lifting. The main goal is to provide a tool for efficient partitions of sets in finite-dimensional spaces with applications in navigation and control design. This objective is achieved by employing a lift-and-project philosophy that relies on performant linear programming optimization and polyhedra manipulation. Once a PWA lifting is constructed, the related polyhedral partition provides an interconnection graph that can be used for path planning, which is considered here as the main application. The paper provides the main theoretical elements of the technique’s foundation and describes the toolbox’s structure along with the numerical artifacts used for its implementation. Illustrative examples for the practical problems are provided, supporting the claims of efficiency and versatility.

Keywords: Robot control, Precision motion control, Intelligent automation
Abstract: This paper introduces an approach for magnetic field simultaneous localization and mapping by leveraging Reduced-Rank Gaussian Process Regression. The proposed algorithm aims to improve the efficiency and accuracy of magnetic field-based localization in environments with spatial variations. The methodology involves representing the magnetic field potential as a sum of basis functions. The use of Reduced-Rank Gaussian Process Regression facilitates a streamlined representation, enabling faster computation and reduced storage requirements. Then, two estimation methods are designed: an Extended Kalman Filter and Iterative Extended Kalman Filter methods to estimate the states of the dynamic model. Simulation results have demonstrated the effectiveness of the proposed approaches in estimating the true dynamic states, with slight improvement of the Iterative Extended Kalman Filter accuracy at certain magnetic field length scales, compared to the Extended Kalman Filter design.

Keywords: Localization, navigation and mapping, Feature extraction, grouping and segmentation, Learning and Statistical methods
Abstract: Localization is the ability for a mobile robot to know its position at all times. When the initial position is unknown, the localization process has to manage several possible positions that could correspond to the real one. The main drawback of this technique is the cost of computational complexity that could be high. In this paper, we present a new way to determine the set of initial possible positions that is fast (less than 3s) and enables to start the localization process with a small number of possible positions. The consequence is that our localization process determines the real position in a fast and robust way. Experimental results show the benefits of the method.

Keywords: Adaptive control, Nonlinear systems, Fuzzy systems
Abstract: This paper studies the prescribed-time tracking problem for nonlinear systems with an uncertain parameter leader. Due to the important role of leaders in the tracking controller design, a leader dynamic observer with the prescribed-time performance is firstly designed to estimate the uncertain leader system while ensuring that the estimation error can approach the origin in the pre-given finite time. In addition, due to the presence of the uncertain parameter leader dynamics in the virtual controller, the derivative of the virtual controller is unknown, which makes the backstepping technique unusable. Thus, a novel filter is constructed by combining with the prescribed-time function to avoid using the virtual controller derivative in the tracking controller design.Further, based on the estimated leader dynamics, an adaptive fuzzy prescribed-time tracking control strategy is proposed by using the backstepping approach, which enables the system output signal can track the uncertain leader without errors in the pre-given finite time. Finally, the vitality of the developed controller is checked by an actual example with comparisons.

Keywords: Cyber-physical systems, Robust control, Cyber security in networked control systems
Abstract: This paper investigates the finite-time sliding mode fault-tolerant secure control issue for cyber-physical systems under period denial-of-service (DoS) jamming attacks and packet dropouts. The considered cyber attacks are periodic DoS attacks， which are modeled by a kind of periodic jamming signals that cause data dropouts on the sensor-to-observer channel. Additionally, an update rule is proposed to characterize packet dropouts caused by intrinsic factors and periodic DoS jamming attacks of wireless channels. Based on the update rule, an observer is presented to obtain the unknown state and fault information. Then, a sliding mode fault-tolerant secure controller is designed using estimated information to ensure the estimated state reaches the sliding surface within a finite time, entering the quasi-sliding mode domain. Moreover, sufficient conditions are provided to guarantee the stochastic finite-time boundedness of the closed-loop system during the whole phase, even in the presence of packet dropouts and DoS jamming attacks. Finally, a simulation result validates the effectiveness and superiority of the proposed method.

Keywords: Feature extraction, grouping and segmentation, Scene analysis, Image-based modeling
Abstract: This paper proposes an improved semantic segmentation model based on Fully Convolutional Network(FCN). Firstly, this paper integrate the channel attention module into the backbone of FCN to identify the channels that are more important for semantic segmentation. Secondly, this paper adds a multi-scale feature fusion module to the network to integrate multi-scale feature information. Thirdly, this paper replaces some of standard convolutions in the backbone network with dilated convolutions to increase the receptive field. Experimental results demonstrate that after adding the two modules, the pixel accuracy can reach 75.8%, and the mIOU value has increased by 2.8% compared to the original FCN model. Moreover, utilizing pre-trained weights during the training process can greatly enhance the performance of the model.

Keywords: Control applications, Distributed optimization and MPC
Abstract: Recent advancements in vehicle technology and autonomous systems necessitate more sophisticated control algorithms, including path-tracking, which is critical for self-driving cars. This paper introduces a path-tracking solution that utilizes an over-actuated platform with steering redundancy, and leverages prior knowledge of road conditions. The prediction of road condition is assumed to be known and available through V2V communication or vision-based predictions. The paper compares the performance of the Stanley Control Law, serving as a benchmark solution, and the Model Predictive Control (MPC) algorithm under varying road friction conditions using a scaled-down vehicle platform. The results demonstrate that the MPC algorithm, with its adaptive capabilities and integrated use of front and rear steering controls, surpasses the Stanley Control Law in maintaining stable and accurate path-tracking under changing conditions.

Keywords: multi-robot systems, Planning, scheduling and coordination, Mobile robotics
Abstract: This paper proposes an online path planning method for multi-robot collision avoidance that accounts for uncertainties in robot positioning. Firstly, the online path planning problem for multiple robots is transformed into a multi-objective optimization problem by considering some necessary factors that affect task efficiency and safety. Notably, the paper considers the uncertainty in robot positions, in which the position follows a Gaussian distribution. Then, by converting probabilistic collision conditions into deterministic constraints, the fitness function ensuring probabilistic collision avoidance is constructed. Next, an improved sine-cosine algorithm is proposed to solve the ideal position. Finally, simulation experiments demonstrate that the proposed method effectively resolves collision issues under probabilistic uncertainty conditions.

Keywords: multi-robot systems, Robot control, Mobile robotics
Abstract: The paper proposes a distributed multi-robot cooperative hunting method in cluttered environments with guaranteed collision avoidance. First, to achieve collision avoidance, a robot safety region collision avoidance method based on Buffered Voronoi Cells (BVC) is proposed. Then, under the BVC collision avoidance framework, a Kalman filer is designed to estimate the evader' position. Next, a hunting cost function based on the evader's position probability density function is constructed. Furthermore, the hunting controller for the pursuers is designed inspired by Voronoi coverage control. Finally, simulation results demonstrate that under the influence of this controller, the distance between the pursuers and the evader continuously decreases until the hunting is successful.

Keywords: Object recognition, Robot sensing and data fusion, Perception systems
Abstract: In the field of autonomous driving and specific environments, target detection is a critical task module. Currently, the mainstream approach to target detection is to use deep learning to train specific network models, enabling them to recognize targets. Although many effective network models have been proposed by scholars, there are few target detection networks tailored for low-light environments. In practical applications, complex lighting changes can lead to decreased accuracy in target detection. This paper combines a low-light enhancement network with a LiDAR-camera fusion target detection network to achieve target detection in low-light environments and validates the algorithm using the Nuscenes benchmark dataset. The experimental results demonstrate that the improved network exhibits greater robustness in target detection under complex lighting conditions.

Keywords: Hybrid systems, Robust control, Identification and estimation
Abstract: This paper proposes a new approach for co-design of interval observer and Fault-Tolerant Control (FTC) for uncertain linear switched systems subject to unknown but bounded disturbances and uncertainties. The lower and upper bounds of system states and fault vectors are reconstructed simultaneously by a Dynamic Proportional Integral Interval Observer (DPIIO) while a fault-tolerant tracking controller is designed to stabilize the closed-loop system and compensate for the effects of faults. The coordinate transformation method is applied to relax the common conservative conditions imposed on observer gain matrices. The observer and controller gain matrices are obtained by Linear Matrix Inequalities (LMIs) based on multiple Lyapunov function using input-to-state-stable (ISS) under average dwell time. Finally, the effectiveness of the proposed strategy is evaluated and proved through an application to lateral vehicle dynamics estimation

Keywords: Marine systems, Search, rescue and field robotics, Space and underwater robots
Abstract: The Coastal underwater evidence search system with surface-underwater collaboration is designed to revolutionize the search for artificial objects in coastal underwater environments, overcoming limitations associated with traditional methods such as divers and tethered remotely operated vehicles. Our innovative multi-robot collaborative system consists of three parts: an autonomous surface vehicle as a mission control center, a towed underwater vehicle for wide-area search, and a biomimetic underwater robot inspired by marine organisms for detailed inspections of identified areas. We conduct extensive simulations and real-world experiments in pond environments and coastal fields to demonstrate the system’s potential to surpass the limitations of conventional underwater search methods, offering a robust and efficient solution for law enforcement and recovery operations in marine settings.

Keywords: Mobile robotics
Abstract: Motion planning for mobile robots involves defining set-points for their locomotion to reachable destinations, while taking into account the robot's dynamics and its interaction with the environment, which may be cluttered with rough terrain. Although the literature offers a wide range of strategies for this task, it remains difficult to get an overview of all available techniques and to choose the right approach for a given use-case. First, this article focuses on identification and classification of main families of motion planning techniques. Then, based on a vast selection of surveys, reviews, case-studies and articles, and through specifically selected criteria, a comparative study of these methods is proposed for the specific use-case of wheeled robots navigating in complex and uneven terrains.

Keywords: Mobile robotics, Perception systems, Robot sensing and data fusion
Abstract: To deploy mobile robots in spaces that they will share with humans, mobile robots should have social navigation methods. One important feature to design such methods is the ability to follow a moving person. In this paper, we present how we detect and track a moving person that our mobile robot is following. A low level detection of the moving person followed, to detect the person independently of their position and orientation with respect to the mobile robot, is combined with a sliding window approach to track the moving person. Some experimental results show the robustness of the method on real scenarios.

Keywords: Robot control, Robust control, Medical robots and bio-robotics
Abstract: This paper deals with the problem of robust trajectory tracking of a robot interacting with a human and subject to uncertainties and the problem of chattering in sliding mode. Indeed, a new controller for robotic manipulator systems using terminal model-based sliding manifolds is proposed. Moreover, a bi-power reaching law with variable-gains is designed to reduce the phenomenon of chattering and to ensure fixed-time stability of the exoskeleton robot trajectories into the sliding manifolds. The chattering is not reduced thanks to the new reaching law only but also thanks to the designed model-based sliding manifolds that allow a decoupled control inputs. The proposed controller is experimentally implemented on an upper-limb rehabilitation exoskeleton robot with seven rotary joints. A Comparison study with super-twisting second-order sliding mode is also presented to show the effectiveness of the developed technique.

Keywords: Search, rescue and field robotics, Perception systems, Neural networks
Abstract: To rescue human life trapped beneath the rubble (invisible to human or visual sensors) in the aftermath of an earthquake, we propose a human signature-based system that does three things efficiently - detects human life, its location, and the safe rescue path. This work has two novel contributions: a) The multi-robot rescue team equipped with our designed WiFi-based transmitter-receiver system to inspect the calamity site and detect human life without entering the rubble. b) The real-world earthquake rubble data is sparsely available and varies uncertainly across countries. We propose a system to collect human signatures unobtrusively pre-calamity, which helps in accurate human life detection post-calamity. We handle the data sparsity and uncertainty problem by proposing data decomposition and Variational Autoencoding (VAE) methods on the signature data. The results we achieve in a real-world calamity site set up with an Unmanned Ground Vehicle (UGV), drone, and robot manipulator system are promising as we can detect human life with 82% accuracy.

Keywords: Localization, navigation and mapping
Abstract: In this paper, we propose a Lidar-Inertial-Visual multi-sensor fusion SLAM system for dynamic environments. While many state of the art multi-sensor fusion SLAM systems can achieve excellent performance in static environment, achieving high accuracy and robustness in dynamic environment remains a significant challenge. In order to address the dynamic object elimination issue during mapping, a dynamic voxel judgment method based on octree map is proposed. Specifically, we per-form template matching between the current frame and sub-map to calculate the dynamic voxel occupancy rate of point clouds in Box, so as to effectively detect dynamic objects in the environment. At the same time, we continue to track dynamic objects and eliminate them in the map. Extensive experiments have been conducted on the KITTI dataset and our own cam-pus dataset. Results shows the proposed method can effectively eliminate dynamic objects and improve the SLAM accuracy in dynamic environments

Keywords: Localization, navigation and mapping
Abstract: We introduce REDO-SLAM, a robust visual SLAM system specifically designed for dynamic environments, which addresses a major drawback in many existing SLAM techniques. Our approach optimally selects a model for segmenting dynamic objects by combining a computationally efficient optical flow neural network with segmentation models, reducing the number of frames needed for applying resource-intensive semantic segmentation models. By using optical flow values, we can predict object masks, enabling the system to effectively manage dynamic objects. We also employ deep reinforcement learning (DRL) to determine the most appropriate frames for accurate mask prediction based on motion-aware rewards. We assessed our method using the TUM and Kitti datasets, showcasing its outstanding performance in dynamic settings. Our method significantly lowers computation time and maintains satisfactory performance in obtaining keyframe trajectories. In comparison to recent visual SLAM systems, our approach is more scalable, efficient, and resilient.

Keywords: Multi-agent systems, Networked control systems, Cooperative control
Abstract: Existing fully distributed protocols for flocking are built upon the network of mobile agents with only cooperative interactions. Rather than investigating such networks, this paper deals with the problem of leader-follower flocking control over signed communication networks, where there impose less restrictions on the distributions of cooperations and competitions in the network of mobile agents. Firstly, for the second-order dynamics model, a novel state feedback controller that relies only on the relative velocity information of neighboring agents is developed. Secondly, the solvability of flocking control problem is transformed to the asymptotic stability of error system, and the latter is guaranteed by treating the product convergence of infinite super-stochastic matrices. Then, sufficient condition for the solvability of flocking control problem is proposed by establishing the inequality constraints on positive and negative edge weights. Finally, a numerical example is performed to illustrate the correctness of the theoretical result.

Keywords: multi-robot systems, Mobile robotics
Abstract: Maximizing the efficiency of multi-robot systems is one of the primary objectives in solving the multi-robot coverage path planning (mCPP) problem. During coverage tasks, unexpected imbalances in the multi-robot systems, such as changes in speed, can lead to suboptimal utilization of the system’s capabilities, subsequently reducing the efficiency of task execution. In this paper, we developed a multi-robot dynamic spanning tree coverage (MDSTC) algorithm, an online area-division-based approach that adjusts region allocation among robots based on their coverage status through an exchange-based mechanism to enhance multi-robot system efficiency. To validate the effectiveness of the proposed algorithm, we conducted extensive numerical simulations. The results demonstrate that our algorithm achieves a superior solution in scenarios where robot efficiency remains balanced, closely matching the performance of state-of-the-art mCPP methods. Furthermore, when differences in efficiency arise among the robots during task execution, the proposed algorithm provides a dynamic solution that enhances overall coverage efficiency.

Keywords: Multi-agent systems, multi-robot systems, Cyber-physical systems
Abstract: Autonomous underwater vehicles (AUVs) are being researched for maritime applications such as ocean exploration and underwater maintenance. A swarm of AUVs is used for complex missions, with each AUV given a specific part of the mission. Energy-aware path planning is crucial for increasing operation time and reliability. This article discusses different path planning strategies for AUV swarms, comparing a particle swarm optimizer (PSO) and a greedy approach to determine the most energy-efficient route. One challenge here is to divide the total number of waypoints among the AUVs reasonably. For this purpose, we investigate a k-means algorithm and an extended variant of the PSO, in which we distribute the clusters dynamic according to a balanced metric. The aim is to find an algorithm that can be used for different applications and validated by different test scenarios focussing on energy consumption and mission fulfillment.

Keywords: multi-robot systems, Sensor networks, Intelligent systems
Abstract: In this paper, we tackle the problem of localizing the source of a three-dimensional signal field with a team of mobile robots able to collect noisy measurements of its strength and share information with each other. The adopted strategy is to cooperatively compute a closed-form estimation of the gradient of the signal field that is then employed to steer the multi-robot system toward the source location. In order to guarantee an accurate and robust gradient estimation, the robots are placed on the surface of a sphere of fixed radius. More specifically, their positions correspond to the generators of a constrained Centroidal Voronoi partition on the spherical surface. We show that, by keeping these specific formations, both crucial geometric properties and a high level of field coverage are simultaneously achieved and that they allow estimating the gradient via simple analytic expressions. We finally provide simulation results to evaluate the performance of the proposed approach, considering both noise-free and noisy measurements. In particular, a comparative analysis shows how its higher robustness against faulty measurements outperforms an alternative state-of-the-art solution.

Keywords: Intelligent systems, Complex systems, Multi-agent systems
Abstract: —The integration of connected autonomous vehicles (CAV) on open roads has gained significant progress in addressing road safety. These vehicles use advanced sensor technology to perceive and react to the road environment, reducing accident risks. Despite these advancements, limitations persist in their perception capabilities. To overcome these limitations, interest is growing in using Unmanned Aerial Vehicles (UAVs) for traffic surveillance, offering extensive coverage and enhanced responsiveness over fixed sensors. In this article, by tackling an optimization problem in road safety using Particle Swarm Optimization (PSO), we particularly focus on a situation where a random flow of vehicles aims to navigate an intersection safely. We compare two scenarios with and without the assistance of an UAV: one where vehicles autonomously manage their speed, and another one where an UAV improve traffic management. Simulation results underscore the pivotal role of drone-assisted vehicles in enhancing road safety, compared to sensors embedded within CAVs. Towards the end of the article, we explore the efficiency of the drone strategy by addressing the issue of delay in the acceptance and implementation of optimal speed instructions, comparing scenarios with and without this delay.

Index Terms—CAV, UAV, drone to vehicle communication (U2V), optimization, road safety, Acceptability, Multi-Agent system.

Keywords: Consensus algorithms, Cooperative control, Networked control systems
Abstract: This paper examines matrix-scaled consensus problems on switching networks where each agent holds time-varying matrix-valued scaling matrices that are either positive definite or negative definite. Matrix-scaled consensus amongst all agents can be achieved if the switching networks have frequent spanning trees and the scaling matrices remain unchanged within a time interval with a spanning tree. On time-varying networks, agents with the same time-varying scaling matrix will converge to the same point, differing from the virtual consensus value by the inverse of the scaling matrix. Both discrete-time and continuous-time cases are discussed. Simulation results are provided to demonstrate the theory.

Keywords: Event-triggered and self-triggered control
Abstract: In this study, the properties of inter-event times in probability for stochastic linear event-triggered control systems are explored. The analysis of inter-event intervals is conducted for three distinct classes of event-triggering mechanisms: the absolute, relative, and mixed ones. Given the inherent stochastic nature of systems, it is challenging to achieve a certain judgment on avoiding Zeno behavior, where events occur at an infinite frequency. To address this issue, a probabilistic approach to examine the inter-event times is employed. It enables us to quantify the likelihood of Zeno behavior occurring in different scenarios, providing valuable insights into the performance of stochastic event-triggered control systems. Our research reveals substantial differences in the likelihood on the occurrence of a positive minimum inter-event interval among different mechanisms. Specifically, the mixed event-triggering mechanism emerges as the most probable one to yield a positive minimum inter-event time, indicating its potential superiority in efficiency. Conversely, some solutions of relative event-triggered control are proved to exhibit Zeno behavior with a probability of 1. Finally, a numerical example is provided to illustrate the efficiency and feasibility of the obtained results.

Keywords: Control applications, Precision motion control, Process control
Abstract: This paper discusses two types of 3D printed materials and their tribological properties in terms of coefficients of sliding friction, rolling friction and coefficient of restitution. The coefficients have been measured experimentally under laboratory conditions and it is compared to other 3D printed materials. After the experiments, a simulation models have been created to verify the data obtained. The data obtained will be used to create simulation models in which the interaction between the grinding bodies and the grinding media takes place in order to increase the accuracy of the simulation models.

Keywords: Control applications
Abstract: This paper proposes a pinch detection method for an automotive power window. Due to a power window system, we can easily rise and lower a window glass only by pressing a switch. However, if the window is rising while there is a finger or an arm between the window frame and the window glass, it might occur a pinching accident. In order to avoid injuries by the accident, an anti-pinch control that detects the pinching and lowers the window glass, is installed in a power window system. In this paper, an anti-pinch control that can detect pinching of the object with both small and large force deflection ratios is proposed. In order to achieve it, the pinch state index is calculated from the motor angular speed and detects pinching when it exceeds the threshold. The main process of calculating the index is realized by a disturbance observer and a non-linear gain. When an object with small force deflection ratio is pinched, the index moves like a pinch force, and when an object with large force deflection ratio is pinched, the index increases faster than a pinch force by the non-linear gain. Performance and effectiveness of the anti-pinch control is evaluated by the experimental data measured on a power window test rig.

Keywords: Process control, Control applications
Abstract: Batch processes are indispensable for the production of low-volume and high-value products. However, control of a batch process is challenging due to the inherent nonlinearity, and time-varying characteristics. In this work, we aim to provide a comparative study of reinforcement learning approaches to perform tracking of a time-varying setpoint trajectory in a batch process. This can be accomplished by defining suitable state vectors, actions, and rewards for batch process control. Upon discretizing the state and action spaces, we utilize two model-free reinforcement learning methods. The efficacy of these two reinforcement learning methods is demonstrated in a non-isothermal batch reaction system. Further, we compare the performance of these reinforcement learning controllers with the nonlinear model predictive controller.

Keywords: Process control, Control applications
Abstract: van de Vusse reaction system in a continuous stirred tank reactor is a benchmark example to study the control of nonlinear non-minimum phase systems. Although different control strategies exist to deal with such systems, the controller's performance strongly depends on the accuracy of the process model. In this work, we develop a model-free, deep reinforcement learning-based control strategy to perform setpoint tracking control. In particular, we develop a deep Q-learning-based controller and demonstrate the tracking performance for different reference trajectories of the desired product concentration. Furthermore, we illustrate the disturbance rejection ability of the proposed deep RL controller.

Keywords: Data analytics., Process control
Abstract: To address the difficulty of fault feature extraction in rolling bearings, caused by the non-stationarity of vibration signals under variable speed conditions and susceptibility to noise interference, a fault diagnosis method based on BAACMD-IDBO-IMCKD and COT is proposed. Firstly, the signal is decomposed using the Bandwidth-aware Adaptive Chirp Mode Decomposition (BAACMD) algorithm and then reconstructed according to the Gini Index (GI). Secondly, the parameters of the Improved Maximum Correlation Kurtosis Deconvolution (IMCKD) are optimized using the Improved Dung Beetle Optimization (IDBO) algorithm that enhanced with Levy flight and t-distribution strategies. Subsequently, the optimized IMCKD is applied to denoise the reconstructed signal. Finally, the bearing Fault Characteristic Orders (FCO) are obtained after Computed Order Tracking (COT). The results demonstrate that this method effectively reduces noise interference and accurately extracts the FCO of variable speed bearings.

Keywords: Smart grid, Multi-agent systems, Energy management systems
Abstract: In the past few years, energy management strategies based on multi-agent reinforcement learning (MARL) have been an active research topic. However, existing MARL algorithms require a large amount of data for training, making it challenging to data privacy. To address the problem, this paper investigates FL-MARL that combines federated learning (FL) with MARL. FL allows each agent to train based on local data and only share model parameters, which means that the actual data are not shared among agents. The framework is in conjunction with a MARL algorithm: independent proximal policy optimization (IPPO). The proposed algorithm has two advantages: 1) It can protect data privacy of each agent and 2) It can adapt to large-scale and decentralized data scenarios. Finally, the performance of the algorithm is verified by simulation.

Keywords: Precision motion control, Modeling and identification, Robot control
Abstract: This work involved finding optimal PID gains for a nonlinear coupled mechanism. A parallel mechanism was presented as an example of such a mechanism. An accurate simulation that corresponds to the physical system was created, and friction parameters were identified through an optimization using a genetic algorithm. Subsequently, another optimization was performed to find optimal PID gains. Remarkable error reductions of 81% and 92% were observed in the experiments after implementing the optimal PID gains on the physical system, without any fine-tuning.

Keywords: Control of biological systems, Robust control
Abstract: Anesthesia plays a crucial role in every surgery. Clinicians are faced with numerous conditions that need to be monitored and controlled. A computerized monitoring and control system would help them in this challenging task. Designing a control law for general anesthesia presents numerous challenges, particularly due to the nonlinear mapping of outputs and the complexity of achieving a feasible input trajectory. While the internal states of the mathematical model are linear, the output exhibits nonlinear behavior. This paper introduces a decomposition of the standard pharmacokinetic-pharmacodynamic (PK-PD) model to develop an effective control strategy for drug administration in anesthesia. It is proposed a method to control the bispectral index (BIS), the main measure of unconsciousness of the patient. This approach aims to optimize anesthesia delivery by maintaining the BIS within a desired range, thereby enhancing patient safety and comfort. The methodology involves designing a fractional order controller to accurately track BIS reference values and adjust anesthetic infusion rates. The effectiveness of the controller is validated through simulations, demonstrating its potential to enhance the precision of anesthesia management. Key findings reveal that such type of control outperforms traditional methods in maintaining target BIS levels, minimizing the risks of over- or under-dosing.

Keywords: Adaptive control, Robot control, Intelligent systems
Abstract: In this paper, we present a reliable, scalable, time deterministic, model-free procedure to tune swarms of Micro Aerial Vehicles (MAVs) using basic sensory data. Two approaches to taking advantage of parallel tuning are presented. First, the tuning with averaging of the results on the basis of performance indices reported from the swarm with identical gains to decrease the negative effect of the noise in the measurements. Second, the tuning with parallel testing of varying set of gains across the swarm to reduce the tuning time. The presented methods were evaluated both in simulation and real-world experiments. The achieved results show the ability of the proposed approach to improve the results of the tuning while decreasing the tuning time, ensuring at the same time a reliable tuning mechanism.

Keywords: multi-robot systems, Robot control
Abstract: We present a pursuit law for capturing a moving target with multiple aerial drones in a bounded environment. The objective is to develop strategies to allow a set of drones to perform a mission cooperatively to complete the task. The presented method combines two established pursuit laws: Group Deviated Pure Pursuit (GPP) and Proportional Navigation Guidance (PNG), and the resulting control architecture is in a cascade form. The results were validated in simulation and experimentation on quadrotor aerial drones. The obtained results confirmed the efficacy of the group mixed pursuit strategy, especially in the case of an agile and faster target. A video of the experimentation result can be seen on: https://youtu.be/cRKRUOV-lV4.

Keywords: Robot control, Control applications, Man-machine interactions
Abstract: This paper focuses on the design, implementation and testing of a Digital Twin (DT) application for a assembly/ disassembly (A/D) flexible robotic cell (FRC) assisted by ABB robotic manipulator (RM). A 3D virtual model is implemented in Siemens NX Mechatronics Concept Designer (MCD). S7-1200 PLC is programmed in Siemens TIA Portal, for both FRC and virtual model control, the last one based on inverse kinematics computation. A centralized architecture is applied for virtual FRC implementation and validation, to enable product simulation and for the automation control of the A/D manufacturing process. As part of the DT technology, Virtual Commissioning (VC) concept is used, to increase the production and performance, by optimizing tasks and to improve the traceability, by monitoring, analyzing and tracking every aspect of all manufacturing system. Moreover, VC significantly reduces potential human risks, delays and costs associated with the real on-site commissioning. To connect the FRC’s PLC controllers with the NX MCD virtual plant model and for controlling the virtual model, Hardware-in-the-Loop (HIL) configuration and simulation strategy is adopted. Therefore Soft Commissioning approach is implemented and performed, by testing the designed PLC software and the behavior of the system using the real physical PLC. A SCADA application is also implemented, acting as a DT operator panel for process monitoring, data acquisition, analysis and visualization of the entire mechatronic system.

Keywords: Robot control, Control applications, multi-robot systems
Abstract: This paper introduces a new approach to achieving autonomous soft-landing of an unmanned aerial vehicle (UAV) on a moving platform. The proposed method incorporates terminal constraints on position, velocity, and acceleration by utilizing a minimum jerk-based guidance strategy. An optimal landing time is computed using an acceleration-minimizing cost function. Software-in-the-loop simulations are conducted using the PX4-ROS2-Gazebo environment to demonstrate successful landing on a moving platform. The results indicate that the proposed strategy generates smooth trajectories, enabling the UAV to autonomously achieve soft-landing on the moving platform, even when perturbations in the platform's motion are introduced.

Keywords: Robot control, Mobile robotics
Abstract: This paper presents a novel method to maintain the dynamic balance of a Mobile Manipulator (MM) during the pick-up of heavy objects. The approach entails the generation of a preliminary reach-to-grasp trajectory, which is subsequently refined by an Optimization-Based Controller (OBC) formulated as a Quadratic Program (QP). The trajectory is modified in a minimal fashion to ensure that the robot maintains balance during the reaching phase and remains balanced when the payload is grasped. This is accomplished by incorporating a balance constraint into the OBC that predicts the Zero Moment Point (ZMP) position of the robot at the beginning of the pick-up phase. This accounts for the gravitational and inertial effects that the object has on the robot. The method is validated through simulations conducted with the TIAGo robot in Gazebo. The results demonstrate that the proposed approach effectively prevents the robot from tipping over when the payload is considered.

Keywords: Robot control, Mobile robotics
Abstract: This work presents a body velocity control strategy for quadruped robots. Such control typically requires accurate kinematic and dynamic model knowledge, which is very challenging because of the multidimensional input-output system and the ground contact. Based on the inverse kinematics, we propose a Proportional-Derivative controlled robot that uses Iterative Learning Control to learn discrete body velocities, which are then generalized using the Gaussian Process Regression model for each joint separately. This controller design enables onboard control and learning in real-time without any simulation. This study illustrates the effectiveness of the proposed methodology over a range of velocities while emphasizing the minimal computational effort associated with its application in a practical context.

Keywords: Cooperative control, multi-robot systems, Multi-agent systems
Abstract: The paper focuses on modeling and experimental evaluation of a quadcopter team configurable coordination guided by a single quadruped robot. We consider the quadcopter team as particles of a two-dimensional deformable body and propose a two-dimensional affine transformation model for safe and collision-free configurable coordination of this heterogeneous robotic system. The proposed affine transformation is decomposed into translation, that is specified by the quadruped global position, and configurable motion of the quadcopters, which is determined by a nonsingular Jacobian matrix so that the quadcopter team can safely navigate a constrained environment while avoiding collision. We propose two methods to experimentally evaluate the proposed heterogeneous robot coordination model. The first method measures real positions of quadcopters, quadruped, and environmental objects all with respect to the global coordinate system. On the other hand, the second method measures position with respect to the local coordinate system fixed on the dog robot which in turn enables safe planning the Jacobian matrix of the quadcopter team while the world is virtually approached the robotic system.

Keywords: Cooperative control, Adaptive control
Abstract: In this paper, we study the optimal control problem of a linear quadratic stochastic system where the randomness results from multiplicative noises. Especially, two controllers having access to different information are involved in the system. Different from most of the existing results which are based on the condition that the information of multiplicative noise is known during the design of optimal controllers, we focus on a more general case that the statistical information of the multiplicative noise is inaccessible. Under this setting, we propose a stochastic approximation algorithm to derive the solutions to algebraic Riccati equations (AREs) and obtain the optimal and stabilizing decentralized controllers.