Multi-vehicle Systems Research Articles

Dynamic Stability Analysis and Optimization of Multi-Vehicle Systems in Heterogeneous Connected Traffic

This study investigates the stability and performance of mixed-traffic flows consisting of human-driven vehicles (HDVs), connected autonomous vehicles (CAVs), autonomous vehicles (AVs), and connected human-driven vehicles (CHVs). Recognizing the complexity introduced by multi-vehicle interactions in such heterogeneous traffic, a refined CAV car-following model that integrates multi-vehicle state information, including headway, weighted velocity differences, weighted acceleration, and optimal velocity memory effects from both front and rear vehicles, is introduced. Through theoretical analysis of the model’s linear and nonlinear stability, the key parameters that enhance flow stability in mixed environments are determined. Numerical simulations across braking, start-up, and ring road scenarios validate the proposed model’s efficacy, demonstrating that it can effectively suppress traffic congestion and reduce oscillations, thereby improving traffic flow stability. This work offers valuable insights into the behavior of connected vehicles within mixed traffic and highlights the potential for CAV-based strategies to enhance both safety and efficiency in future transportation systems.

Data-Driven Formation Control for Multi-Vehicle Systems Induced by Leader Motion

In this paper, a leader motion mechanism is studied for the finite time achievement of any desired formation of a multi-agent system. The approach adopted in this paper exploits a recent technique based on leader motion to the formation control problem of second-order systems, with a special effort to networks of mobile devices and teams of vehicles. After a thorough description of the problem framework, the leader motion mechanism is designed to accomplish the prescribed formation attainment in finite time. Both asymptotic and transient behavior are thoroughly analyzed, to derive the appropriate analytical conditions for the controller design. The overall algorithm is then finalized by two procedures that allow the exploitation of local data only, and the leader motion mechanism is performed based on data collected by the leader during a preliminary experimental stage. A final section of simulation results closes the paper, confirming the effectiveness of the proposed strategy for formation control of a multi-agent system.

Distributed model predictive control for consensus of nonlinear systems via parametric sensitivity

To handle the nonlinear consensus problem, a distributed model predictive control (DMPC) scheme is developed via parametric sensitivity. A two-stage input computation strategy is adopted for enhancing optimization efficiency. In the background stage, each agent first establishes its next-step optimization problem based on communication topology, and then performs distributed optimization to calculate the future inputs. In the online stage, all the agents build their sensitivity equations based on new information. Three variants of sensitivity equation are developed based on the level of communication load capacity, and the corresponding computation strategies are proposed. After solution, the background inputs are corrected and implemented. The optimality and robustness of the proposed algorithm are rigorously derived. Finally, the superiority of this DMPC scheme is demonstrated in the multi-vehicle system with two different topologies.

Path-Guided Formation-Containment Control for Networked Heterogeneous Multi-Vehicle Systems

Path-Guided Formation-Containment Control for Networked Heterogeneous Multi-Vehicle Systems

Output synchronization of a class of complex dynamic networks: A reinforcement learning method

In this paper, to achieve the synchronization control for a class of complex dynamic networks with completely unknown system dynamics, a reinforcement learning output feedback algorithm based on state reconstruction is proposed. Given the high cost and complexity associated with obtaining the full state information, an output-based node state reconstruction method is employed for the first time in complex dynamic networks. The proposed method utilizes a sequence composed of a finite number of output data to reconstruct the current state. At the same time, the overall error system is constructed to handle the coupling relationship between nodes, to facilitate the controller design. Thereafter, considering the system dynamics are unknown, an algorithm based on reinforcement learning is proposed to ensure rapid synchronization of node outputs, and the convergence of proposed method is proven. Finally, the feasibility of proposed algorithm is corroborated through a simulation example and a multi-vehicle system.

Interval Split Covariance Intersection Filter: Theory and Its Application to Cooperative Localization in a Multi-Sensor Multi-Vehicle System.

The data incest problem causes inter-estimate correlation during data fusion processes, which yields inconsistent data fusion results. Especially in the multi-sensor multi-vehicle (MSMV) system, the data incest problem is serious due to multiple relative position estimations, which not only lead to pessimistic estimation but also cause additional computational overhead. In order to address the data incest problem, we propose a new data fusion method termed the interval split covariance intersection filter (ISCIF). The general consistency of the ISCIF is proven, serving as supplementary proof for the split covariance intersection filter (SCIF). Moreover, a decentralized MSMV localization system including absolute and relative positioning stages is designed. In the absolute positioning stage, each vehicle uses the ISCIF algorithm to update its own position based on absolute measurements. In the relative position stage, the interval constraint propagation (ICP) method is implemented to preprocess multiple relative position estimates and initially prepare input data for ISCIF. Then, the proposed ISCIF algorithm is employed to realize relative positioning. In addition, comparative simulations demonstrate that the proposed method can achieve both accurate and consistent results compared with the state-of-the-art methods.

Finite-Time Fault-Tolerant Formation Control for Distributed Multi-Vehicle Networks With Bearing Measurements

This paper addresses a bearing-only formation tracking problem in robotic networks by considering exogenous disturbances and actuator faults. In contrast to traditional position-based coordination strategies, the bearing-only coordinated movements of the unmanned vehicles only rely on the neighboring bearing information. This feature can be utilized to reduce the sensing requirements in the hardware implementation. A gradient-descent protocol is first developed to achieve the desired coordination within a prespecified settling time, where the unknown disturbances are considered in the vehicle dynamics, then the bound of formation tracking error is guaranteed by the Lyapunov approach. In case of damage to the actuators (e.g., motors) in some of the vehicles during the task, fault-tolerant analysis of the proposed controller is provided to ensure the success of the task in extreme environments. Furthermore, the proposed bearing-based method is extended to deal with general linear systems, which can be applied to a wider range of robotic platforms. Finally, numerical simulations and lab-based experiments using unmanned ground vehicles are conducted to validate the effectiveness of the proposed strategy. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The aim of this paper is to develop and design a practical bearing-only formation control approach for multi-vehicle systems. Many real-world complex tasks can be solved by multiple unmanned aerial and ground vehicles being connected by a communication network. This paper has proposed a formation tracking scheme for networked multi-vehicle systems that only relies on the relative bearing information of the neighboring vehicles. Closed-loop stability of the scheme and finite-time convergence of the tracking error have been established using the Lyapunov stability approach. The proposed method ensures the robustness and fault-tolerance of the multi-vehicle system against hardware faults or exogenous disturbances. A systematic set of guidelines on how to apply the proposed strategy in practice is also provided for the control practitioners in the form of an algorithm. In order to demonstrate the feasibility and usefulness of the proposed coordination scheme, numerical simulations and lab-based hardware experiments were conducted. Potential applications of the proposed scheme include search and rescue, security surveillance and cooperative exploration.

Constraint-following adaptive robust control for multi-vehicle system formation with collision avoidance

For the formation on unstructured road with complex road conditions, the rationality of constraint design and uncertainty compensation seriously affect the connected and autonomous vehicle (CAV) system performance. The system constraints are studied for safe and efficient formation driving, and an adaptive robust formation control scheme is proposed. First, for the desired configuration and global collision avoidance, a series of equality and unilateral inequality constraints are designed for inter-vehicle spacing. Second, a constraint handling scheme is proposed based on the diffeomorphism method, adjustable transformation function, and bounded constraint. Third, (possibly fast) time-varying but bounded multi-source uncertainty is considered, and then a self-adjusting leakage-type adaptive law is proposed to estimate the integrated uncertainty bound online. Fourth, the performance measure β is defined to evaluate the constraint-following error, and an adaptive robust control is proposed. The effectiveness of the proposed control scheme is verified by simulation. The results show that the proposed control renders the error to be uniformly bounded and uniformly ultimately bounded. The CAV system approximately follows the constraints, regardless of the uncertainty.

Cooperative Vehicle Localization in Multi-Sensor Multi-Vehicle Systems Based on an Interval Split Covariance Intersection Filter with Fault Detection and Exclusion

In the cooperative multi-sensor multi-vehicle (MSMV) localization domain, the data incest problem yields inconsistent data fusion results, thereby reducing the accuracy of vehicle localization. In order to address this problem, we propose the interval split covariance intersection filter (ISCIF). At first, the proposed ISCIF method is applied to the absolute positioning step. Then, we combine the interval constraint propagation (ICP) method and the proposed ISCIF method to realize relative positioning. Additionally, in order to enhance the robustness of the MSMV localization system, a Kullback–Leibler divergence (KLD)-based fault detection and exclusion (FDE) method is implemented in our system. Three simulations were carried out: Simulation scenarios 1 and 2 aimed to assess the accuracy of the proposed ISCIF with various capabilities of absolute vehicle positioning, while simulation scenario 3 was designed to evaluate the localization performance when faults were present. The simulation results of scenarios 1 and 2 demonstrated that our proposed vehicle localization method reduced the root mean square error (RMSE) by 8.9% and 15.5%, respectively, compared to the conventional split covariance intersection filter (SCIF) method. The simulation results of scenario 3 indicated that the implemented FDE method could effectively reduce the RMSE of vehicles (by about 55%) when faults were present in the system.

Resilient Distributed Source Localization for Multi-Vehicle Systems Under Sybil Attacks

Resilient Distributed Source Localization for Multi-Vehicle Systems Under Sybil Attacks

Manipulation of the Multi-Vehicle System for the Industrial Applications

This approach should indicate some challenges in routing and scheduling for the multi-vehicle system. The proposed method delivers a novel method to generate the free-collision trajectory as well as optimal route from starting point to destination. The estimated time at one node and the classification of load level support vehicle to decide which proper route is and stable movement is reached. From these results, it could be observed that the proposed approach is feasible and effective for many applications. The proposed method for routing and scheduling might be useful in the multi-vehicle system. In the large scale system, some intelligent schemes should be considered to integrate.

Comparative Analysis of Centralized and Federated Learning Techniques for Sensor Diagnosis Applied to Cooperative Localization for Multi-Robot Systems.

Cooperation in multi-vehicle systems has gained great interest, as it has potential and requires proving safety conditions and integration. To localize themselves, vehicles observe the environment using sensors with various technologies, each prone to faults that can degrade the performance and reliability of the system. In this paper, we propose the coupling of model-based and data-driven techniques in diagnosis to produce a fault-tolerant cooperative localization solution. Consequently, prior knowledge can guide a discriminative model that learns from a labeled dataset of appropriately injected sensor faults to effectively identify and flag erroneous readings. Going further in security, we conduct a comparative study on learning techniques: centralized and federated. In centralized learning, fault indicators generated by model-based techniques from all vehicles are collected to train a single model, while federating the learning allows local models to be trained on each vehicle individually without sharing anything but the models to be aggregated. Logistic regression is used for learning where parameters are established prior to learning and contingent upon the input dimensionality. We evaluate the faults detection performance considering diverse fault scenarios, aiming to test the effectiveness of each and assess their performance in the context of sensor faults detection within a multi-vehicle system.

Byzantine Resilient Joint Localization and Target Tracking of Multi-Vehicle Systems

This work investigates the problem of joint localization and target tracking (JLATT) of multi-vehicle systems (MVSs) in the presence of Byzantine attacks (BAs). For MVSs, there may exist malicious and misbehaving vehicles, called Byzantine vehicles, which act as deceivers and pass false information to their neighbors. For the first time, we formulate a resilient JLATT framework to synthesize the hybrid problems of distributed cooperative localization and flock tracking in the presence of BAs. More specifically, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">covariance weighted mean-subsequence-filter</i> (CW-MSF) algorithm to guarantee that the MVS achieves flock tracking w.r.t a target even in the presence of a certain fraction of Byzantine vehicles. Note that this resilient JLATT scheme does not require global information and thus it is fully distributed. The practicality and validity of this novel JLATT framework are verified via an illustrative numerical simulation example.

Distributed formation control of multi-vehicle system based on hybrid relative measurements

Formation control has been studied extensively along with the potential applications of multi-vehicle system in various areas. In this paper, a distributed control scheme based on the relative bearing and distance measurements is proposed. The main difference from the existing works lies in that the proposed control scheme can realize unambiguous formation tracking while considering the task-specification and environment constraints under practical bearing and distance sensing conditions. First, the uniqueness of the formation based on relative bearings and distances is investigated and the sufficient condition is provided. Further, the distributed formation controllers that are able to dynamically track the formation with both leaderless and leader-follower setups are designed under practical sensing conditions. In the end, to validate the effectiveness and practicability of the proposed distributed hybrid measurements based formation control scheme, both simulations based on synthetic scenarios and experiments with quadrotors equipping cameras and Ultra-Wide Band (UWB) transmitters are carried out. The simulation and experiment results show that the proposed controllers are able to track the desired formation pattern based on the bearing and distance measurements.

Distributed Bearing-Only Formation Control for UAV-UWSV Heterogeneous System

This paper investigates the bearing-only formation control problem of a heterogeneous multi-vehicle system, which includes unmanned aerial vehicles (UAVs) and unmanned surface vehicles (UWSVs). The interactions among vehicles are described by a particular class of directed and acyclic graphs, namely heterogeneous leader-first follower (HLFF) graphs. Under the HLFF structure, a UAV is selected as the leader, moving with the reference dynamics, while the followers, including both UAVs and UWSVs, are responsible for controlling the position with regard to the neighbors in the formation. To solve the problem, we propose a velocity-estimation-based control scheme, which consists of a distributed observer for estimating the reference velocity of each vehicle and a distributed formation control law for achieving the desired formation based on the estimations and bearing measurements. Moreover, it is shown that the translation and scale of the formation can be uniquely determined by the leader UAV. The theoretical analysis demonstrated the finite-time convergence of the velocity estimation and the asymptotic convergence of the formation tracking. Comparative simulation results are provided to substantiate the effectiveness of the proposed method.

A Novel Multi-Agent Model-Free Adaptive Control Algorithm for a Class of Multivehicle Systems with Constraints

To solve the problem of longitudinal cooperative formation driving control of multiple vehicles, a model-free adaptive control algorithm with constraints (cMFAC) is proposed in this paper. In the cMFAC algorithm, a dynamic linearization technique with a time-varying parameter pseudo-gradient (PG) is used to linearize the multivehicle collaborative system. Then, a cMFAC controller is designed. The algorithm sets the input and output constraints at the same time to prevent the vehicle speed and other parameters from exceeding the specified range. The main advantage of the cMFAC algorithm is that the entire control process only needs the input and output data of each vehicle and can effectively handle the input and output constraints. In addition, the stability of the cMFAC method is verified through strict mathematical analysis, and its effectiveness is verified with semi-physical experiments based on a MATLAB/Simulink module and CarSim platform connection environment. It is worth noting that the proposed cMFAC controller is symmetric because the input cost function and PG cost function have symmetric and similar structures, and the forms of the two cost functions are the same.

Multi-vehicle localization by distributed MHE over a sensor network with sporadic measurements: Further developments and experimental results

This paper proposes a Distributed Moving Horizon Estimation (DMHE) approach performed by an external static Sensor Network (SN) composed of surveillance cameras and their associated low-cost computers. This approach allows to localize a non-cooperative Multi-Vehicle System (i.e. intruder vehicles which do not communicate with the SN) from sporadic measurements. In this context, measurements are available at time instants a priori unknown and the proposed DHME technique is designed to face this issue by resorting to time-dependent parameters in the problem formulation. Moreover, this technique is well-suited to better estimate the state of the intruder vehicles thanks to its capability to efficiently exploit environmental information via constraints. In fact, when dealing with sporadic measurements and biased noisy sensors data, the use of output constraints can contribute to locally enhance the estimation accuracy. In order to confirm its effectiveness, the proposed method is validated on an experimental setup (video presentation available at https://youtu.be/1CkSba2wVuI) within an indoor arena equipped with a motion capture system. Three scenarios are considered for the localization of a non-cooperative Multi-Vehicle System composed of five robots, where the proposed DMHE technique is performed using sporadic position measurements provided by an external static Sensor Network with low-cost cameras (webcams) and computers (Raspberry PI) connected to them. Rigorous comparisons in terms of computation time and accuracy of the estimates highlight the efficacy of the proposed approach.

Observer-Based Event-Triggered Composite Anti-Disturbance Control for Multi-Agent Systems Under Multiple Disturbances and Stochastic FDIAs

This article aims to investigate the security consensus and composite anti-disturbance problems for a class of nonlinear multi-agent systems subjected to stochastic false data injection attacks (FDIAs) and multiple disturbances under a directed communication topology. To attenuate and reject of the negative effects of two types of disturbances, a disturbance observer (DO) is designed to counteract the disturbance produced by exogenous system, and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {H}_\infty $ </tex-math></inline-formula> control method is adopted to attenuate the bounded errors and variables caused by the other type of disturbances and FDIAs simultaneously. To ensure the consensus performance of MASs, an observer-based control strategy is designed, and a novel adaptive compensation technique is proposed to not only evaluate the upper bounds of the unknown but bounded disturbances but also improve the accuracy of the state observer. Furthermore, a novel event-triggered mechanism (ETM) without requiring continuous communication among neighboring agents is developed to reduce the controller update frequency and the communication burden. Meanwhile, Zeno behavior is excluded. Finally, numerical simulations are provided to verify the availability of the designed method. Note to Practitioners—In multi-agent systems, network security is very important. For example, in smart power grid systems, it is necessary to use the method of state estimation to observe the system to guarantee its safe operation. However, the measured value of the instrument may be affected by FDIAs in the transmission process, thus changing the result of state estimation and causing misjudgment of the system. Similarly, in multi-vehicle systems, FDIAs may destroy the location information of vehicles and cause serious accidents. In addition, the system will be subjected to different types of disturbances in practice, thus reducing the performance of the system. In view of the threat of FDIAs and disturbances to the MAS, a composite anti-disturbance method and an observer-based control strategy are proposed. Meanwhile, to avoid the limitation of communication bandwidth in reality, a novel ETM is developed to save network resources.

Integrating Both Routing and Scheduling Into Motion Planner for Multivehicle System

In multi-automated guided vehicle (AGV) control, optimization and collision avoidance are two of the key issues. To deal with these problems of the AGV fleet, motion planning is a good solution. This method usually comprises two steps as follows: routing and scheduling that are always separately executed in conventional routine. This scheme still exists some drawbacks, such as limitation of candidate paths or lack of flexibility in handling collisions. Besides, with a specific layout, the algorithm needs to be modified to be proper with that application. The warehouse with grid-based layout employed popularly in logistics and supply chain is our concern. To overcome this theme, a time-frame-based routing and scheduling (TFRS) algorithm for motion planning of vehicles is proposed for this warehouse application. In detail, TFRS can also be called an enhanced Dijkstra’s algorithm (EDA) with adaptive weights for every segment and node. It was designed to gain several benefits of time due to the shortest path, free collision, and proper for chessboard layout. The main idea is that while conducting path routing, certain circumstances of potential accidents are detected and dealt by scheduling in every loop. Due to simultaneous policies of routing and scheduling, the optimization and secure operation could be achieved in the AGV system. Numerous situations in danger of collision are experimented to verify the effectiveness, flexibility, and correctness of the proposed algorithm.

CoGoV: A Safe Motion Planning Distributed Supervision Framework for Multi-Vehicle Formations

This paper introduces CoGoV: a new Matlab-based toolbox for the simulation of Command Governor supervision strategies applied to distributed motion planning problems for multi-vehicle systems. CoGoV is an open-source and object-oriented software and contains several classes for modeling unmanned vehicles, designing control strategies and solving optimization problems for achieving prescribed tasks in complex simulation scenarios. Because of its modular structure, it can be used to supervise many kinds of autonomous vehicles in marine, terrestrial and aerial domains. Throughout the paper, the benefits of the toolbox are presented by means of simulations involving the supervision and coordination of autonomous marine surface vehicles. The CoGoV toolbox is available at https://github.com/vinz-uts/CoGoV/