Multi-vehicle Systems Research Articles

Robust Consensus Algorithms for Multiscale Coordination Control of Multivehicle Systems With Disturbances

This paper aims to address a new class of multiscale coordination control problems for multivehicle systems that are subject to external disturbances. Each vehicle is measured by an independent scale due to the constraints of physical environments. Based on the nearest neighbor-interaction rules, three algorithms of different convergence rates are presented such that the vehicles are guaranteed to achieve consensus on a common quantity for all but of their own scales. Simulation tests are performed to validate the effectiveness and robustness of these consensus algorithms. By introducing a Lyapunov potential function, the consensus analysis is further achieved for the three multiscale algorithms, regardless of them with an exponential, finite-time, or fixed-time convergence rate and in the presence of bounded disturbances.

Distributed Formation Control of Nonholonomic Vehicles Subject to Velocity Constraints

This paper considers a leader–follower formation control problem of nonholonomic vehicles of unicycle-type subject to velocity constraints. The velocity constraints of each vehicle are described by saturated angular velocity and bounded linear velocity lying between two positive constants. The communication topology of the networked multi-vehicle system is modeled by a directed graph. The designed control law is distributed in the sense that the controller of each follower vehicle only uses its own information and the information of its neighboring vehicles. It is shown that with the proposed control law, the leader–follower formation can be achieved without using absolute position measurements while the velocity constraints are satisfied. Finally, the simulation results of an example verify effectiveness of the proposed control law.

Optimal Control of Multi-Vehicle Systems with Logical Constraints on Graphs

Optimal Control of Multi-Vehicle Systems with Logical Constraints on Graphs

Energy-efficient environmentally adaptive consensus-based formation control with collision avoidance for multi-vehicle systems

Abstract: Technological advances over the past several decades have made it possible to develop increasingly cheap but also increasingly capable and autonomous unmanned marine vehicles. Additionally, this makes cooperative approaches to problem-solving a viable strategy in marine applications. This paper takes a look at a control scenario featuring a system consisting of a number of floating robotic platforms designed with long-term functioning in a challenging environment in mind. Decentralised consensus strategies for formation generation and formation keeping are described, with an introduction of modifications such as virtual forces that ensure collision avoidance. The group of vehicles is then placed in a simulated environment and subjected to environmental disturbances in the form of water current. An additional adaptive algorithm is proposed and implemented in the simulation, allowing the vehicles to assume a formation relative to the measured water current that ensures greater energy efficiency by having the vehicles physically shield each other from the current’s detrimental effects. Finally, the simulation results are discussed and some directions for further work are suggested.

Time-varying formation control for double-integrator multi-agent systems with jointly connected topologies

ABSTRACTTime-varying formation analysis and design problems for double-integrator multi-agent systems with jointly connected topologies are investigated. Different from the previous work on formation control, in this paper, the formation is specified by time-varying piecewise continuously differentiable vectors and the topology can be disconnected at any time instant. First, a distributed formation control protocol is constructed using local neighbour-to-neighbour information. In the case where the switching topology is jointly connected, necessary and sufficient conditions for double-integrator multi-agent systems to achieve time-varying formations are proposed, where the formation feasibility constraint is also derived. To describe the macroscopic movement of the whole formation, explicit expressions of the formation reference are presented, the motion modes of which can be partially assigned. Moreover, an approach to design the formation control protocol is given, which is fully distributed and requires no global information about the topology. Finally, the obtained theoretical results are applied to deal with the time-varying formation control problems of multi-vehicle systems.

Multi-vehicle relative localization based on single range measurements

Most cooperative motion tasks of multi vehicle systems require the agents to share relative localization information. Assuming the relative velocity of the vehicles to be known, under suitable observability conditions, relative localization among a pair of agents can be performed based on single range measurements. The problem addressed consists in designing a relative localization solution for a networked group of vehicles measuring mutual ranges: in particular, the objective is to exploit the presence of intra-vehicle communications to enhance the range-based relative position estimation. Geometrical constraints associated to the agents' (unknown) positions are explicitly accounted for in the estimation schema. The approach brings together a recent single range localization solution with a projection based Kalman filter estimation technique in the presence of state space constraints. Simulation examples are provided showing the effectiveness of the proposed solution.

Online Trajectory Planning Method for Multi-Vehicle System Considering Network Connectivity and Collision Avoidance Simultaneously

This paper proposes a decentralized trajectory planning method by which vehicles stay connected and avoid collisions within a vehicle team by communicating through a multi-hop ad hoc wireless netwo...

アドホックネットワークを形成する群ロボットの優先度付き分散 Receding Horizon Control

This paper presents a multi-robot control method considering a mobile ad-hoc network connectivity and inter-robot collision avoidance. A method I studied previously was based on a receding horizon control (RHC) method, and was decentralized one by some kind of serialization. However, the previous method remains some disadvantages; especially, no-scalability and huge computations. Because these disadvantages are critical flaws for multi-vehicle systems, I strive to improve the method in this paper. By a distributed priority variable, several vehicles (but not all vehicles) can make their decisions at same time in the proposed method. We show the proposed method in this paper is less computation than the previous method.

Mission Assignment for Multi-Vehicle Systems in Industrial Environments

In this paper we introduce a methodology for mission assignment in multiple vehicle system exploited in industrial environments for logistics operations. We explicitly consider the model of the traffic, for performing an optimized dynamic mission assignment. Simulation results are provided for comparing the proposed methodology with state-of-the-art techniques, that do not consider the state of the traffic while allocating missions.

Optimal Control of Multi-Vehicle Systems with Temporal Logic Constraints

Optimal Control of Multi-Vehicle Systems with Temporal Logic Constraints

Cooperative Path-Planning for Multi-Vehicle Systems

In this paper, we propose a collision avoidance algorithm for multi-vehicle systems, which is a common problem in many areas, including navigation and robotics. In dynamic environments, vehicles may become involved in potential collisions with each other, particularly when the vehicle density is high and the direction of travel is unrestricted. Cooperatively planning vehicle movement can effectively reduce and fairly distribute the detour inconvenience before subsequently returning vehicles to their intended paths. We present a novel method of cooperative path planning for multi-vehicle systems based on reinforcement learning to address this problem as a decision process. A dynamic system is described as a multi-dimensional space formed by vectors as states to represent all participating vehicles’ position and orientation, whilst considering the kinematic constraints of the vehicles. Actions are defined for the system to transit from one state to another. In order to select appropriate actions whilst satisfying the constraints of path smoothness, constant speed and complying with a minimum distance between vehicles, an approximate value function is iteratively developed to indicate the desirability of every state-action pair from the continuous state space and action space. The proposed scheme comprises two phases. The convergence of the value function takes place in the former learning phase, and it is then used as a path planning guideline in the subsequent action phase. This paper summarizes the concept and methodologies used to implement this online cooperative collision avoidance algorithm and presents results and analysis regarding how this cooperative scheme improves upon two baseline schemes where vehicles make movement decisions independently.

Passivity-based control for multi-vehicle systems subject to string constraints

In this paper, we show how heterogeneous bidirectional vehicle strings can be modelled as port-Hamiltonian systems. Analysis of stability and string stability within this framework is straightforward and leads to a better understanding of the underlying problem. Nonlinear local control and additional integral action is introduced to design a suitable control law guaranteeing l2 string stability of the system with respect to bounded disturbances.

Decentralized spatial partitioning algorithms for multi-vehicle systems based on the minimum control effort metric

We consider the problem of characterizing a spatial partition of the position space of a team of vehicles with linear time-varying kinematics. The generalized metric that determines the proximity relations between the vehicles and an arbitrary target point in the partition space is the minimum control effort required for each vehicle to reach the latter point with zero miss distance and exactly zero velocity at a prescribed final time. We show that the solution to the generalized Voronoi partitioning problem can be associated with a special class of spatial partitions known as affine diagrams. Because the combinatorial complexity of the affine diagrams is comparable to the one of the standard Voronoi diagrams, their computation does not pose a significant challenge in applications of multi-vehicle systems. Subsequently, we propose an algorithm for the computation of the spatial partition, which is decentralized in the sense that each vehicle can compute an approximation of its own cell independently from the other vehicles from the same team without utilizing a common spatial mesh. Numerical simulations that illustrate the theoretical developments are also presented.

Simultaneous multi-vehicle detection and tracking framework with pavement constraints based on machine learning and particle filter algorithm

Due to the large variations of environment with ever-changing background and vehicles with different shapes, colors and appearances, to implement a real-time on-board vehicle recognition system with high adaptability, efficiency and robustness in complicated environments, remains challenging. This paper introduces a simultaneous detection and tracking framework for robust on-board vehicle recognition based on monocular vision technology. The framework utilizes a novel layered machine learning and particle filter to build a multi-vehicle detection and tracking system. In the vehicle detection stage, a layered machine learning method is presented, which combines coarse-search and fine-search to obtain the target using the AdaBoost-based training algorithm. The pavement segmentation method based on characteristic similarity is proposed to estimate the most likely pavement area. Efficiency and accuracy are enhanced by restricting vehicle detection within the downsized area of pavement. In vehicle tracking stage, a multi-objective tracking algorithm based on target state management and particle filter is proposed. The proposed system is evaluated by roadway video captured in a variety of traffics, illumination, and weather conditions. The evaluating results show that, under conditions of proper illumination and clear vehicle appearance, the proposed system achieves 91.2% detection rate and 2.6% false detection rate. Experiments compared to typical algorithms show that, the presented algorithm reduces the false detection rate nearly by half at the cost of decreasing 2.7%–8.6% detection rate. This paper proposes a multi-vehicle detection and tracking system, which is promising for implementation in an on-board vehicle recognition system with high precision, strong robustness and low computational cost.

Decentralized spatial partitioning for multi-vehicle systems in spatiotemporal flow-field

We consider the problem of characterizing a generalized Voronoi diagram/partition of a convex polygon in a two-dimensional Euclidean space that encodes information about the proximity relations between a team of aerial/marine vehicles and arbitrary points in the partition space. These proximity relations are determined by the time required for each vehicle to reach an arbitrary point (time-to-go) in the partition space when driven by a locally optimal feedback control law in the presence of a spatiotemporal drift field. The main contribution of this work is the presentation of a partitioning algorithm, which is decentralized, in the sense that each vehicle can independently compute its corresponding cell from the generalized Voronoi partition without computing or receiving information about the cells of the other vehicles. Finally, we present numerical simulations using data from real drift fields to illustrate the key features of the decentralized solution to the proposed class of spatial partitioning problems.

Coordinated formation pattern control of multiple marine surface vehicles with model uncertainty and time-varying ocean currents

This paper considers the coordinated formation pattern control of multiple marine surface vehicles in the presence of model uncertainty and time-varying ocean disturbances induced wind, waves and ocean currents. Leaderless and leader-follower formation controllers depending on the information of neighboring vehicles are devised based on a backstepping technique. Neural networks together with adaptive filtering methods are employed to extract the low frequency content of the model uncertainty and ocean disturbances. The results are further extended to the formation pattern control with unmatched time-varying ocean currents. An observer is developed to precisely identify the time-varying ocean currents. Then, observer-based leaderless and leader-follower formation controllers are proposed. For both cases, the stability properties of the multi-vehicle systems are established via Lyapunov analysis, and the formation tracking errors converge to an adjustable neighborhood of origin. An advantage of this design is that it results in adaptive formation controllers with guaranteed low frequency control signals, which facilitates practical implementations. An example is given to show the performance of the proposed methods.

Distributed RHC for Tracking and Formation of Nonholonomic Multi-Vehicle Systems

This paper considers the synchronous distributed receding horizon control (RHC) for a general problem of the nonholonomic multi-vehicle systems, i.e., the simultaneous forward/backward tracking, regulation and formation with the collision avoidance. First, for each vehicle, a positively invariant terminal-state region and an auxiliary controller are developed. When every vehicle lies in its terminal-state region, all the distributed control targets are achieved by the auxiliary controller. Second, the compatibility constraint, restricting the norm of the uncertain deviation between the assumed and actual predictive trajectories of each vehicle, is given, which respects both the collision avoidance and convergence guarantee. Thirdly, a robust collision avoidance constraint tolerating for the uncertain deviation is designed. By these designs, an overall control algorithm is proposed, by applying which all the control targets are achieved. Two illustrative examples are provided to show the advantage and effectiveness of the proposed approach.

Fault‐tolerant output synchronisation control of multi‐vehicle systems

This study presents a fault-tolerant output synchronisation control approach for a class of non-linear multi-vehicle systems. Firstly, adaptive fault diagnosis observers are designed for vehicle systems by utilising local measurements. Adaptive thresholds for distributed fault-detection of each vehicle system are derived. Then an adaptive fault estimation algorithm is proposed. Using the online obtained fault information, an adaptive fault-tolerant control protocol is designed to ensure the output synchronisation of the networked vehicle systems. It is proved that the proposed active fault-tolerant control protocol is capable of maintaining the output synchronisation performance of the multi-vehicle systems, at an acceptable level, in the presence of faults. Finally, simulation results are presented to show the effectiveness of the proposed algorithms.

Swarm-based path-following for cooperative unmanned surface vehicles

This article proposes a swarm-based path-following guidance system for an autonomous marine multi-vehicle system. In particular, a team of unmanned surface vehicles is required to converge to and navigate along a desired reference path, while at the same time aggregating and maintaining a range-based formation configuration. First, a separate description is given for a swarm methodology, initially developed for small ground mobile robots and exploited to aggregate the robot team, and a virtual target–based path-following guidance system developed for unmanned surface vehicles, exploited to drive not the single vehicles but the robot formation as a whole. Then, the integration of the two proposed methodologies is reported and proven, in order to guarantee the feasibility and stability of the overall guidance framework. Simulative results are proposed to validate the effectiveness of the proposed methodology and to evaluate the system performances.

Finite-time optimal formation tracking control of vehicles in horizontal plane

This paper studies the problem of finite-time optimal formation tracking for planar vehicles which are considered as rigid bodies, under the condition that the tracking time is given according to task requirements in advance. By using Pontryagin’s maximum principle (PMP) on a Lie group, an optimal control law is designed for vehicles with holonomic dynamics to track a desired reference trajectory at the given tracking time in the manner of rigid formation which is also specified by task requirements. Simultaneously, a corresponding cost function is considered and guaranteed to be optimal. Then, the above mentioned result of tracking is extended to the case of multi-vehicle systems with a directed-tree communication topology. Furthermore, some conditions are proposed to ensure the adjoint orbits of vehicles to be non-holonomic. Finally, the numerical simulations are provided to illustrate the effectiveness of the theoretical results.