Target tracking and obstacle avoidance for multi-agent networks with input constraints

In this paper, the problems of target tracking and obstacle avoidance for multi-agent networks with input constraints are investigated. When there is a moving obstacle, the control objectives are to make the agents track a moving target and to avoid collisions among agents. First, without considering the input constraints, a novel distributed controller can be obtained based on the potential function. Second, at each sampling time, the control algorithm is optimized. Furthermore, to solve the problem that agents cannot effectively avoid the obstacles in dynamic environment where the obstacles are moving, a new velocity repulsive potential is designed. One advantage of the designed control algorithm is that each agent only requires local knowledge of its neighboring agents. Finally, simulation results are provided to verify the effectiveness of the proposed approach.     

碰撞锥检测改进的多智能体避障算法

避障是多智能体能够适应复杂环境并顺利完成任务的必要条件之一。为使多智能体更快通过障碍物并达到一致,提出了一种多智能体避障控制算法。算法引入了避障系数,该系数由基于角度比较的碰撞锥检测方法来确定,并通过牵制控制输入完成多智能体的避障。证明了在该算法作用下所有智能体最终会避开障碍,避免碰撞并最终达到一致。通过实验仿真分析和对比,该方法能够使得多智能体更快避开障碍物。     

一种多约束下无人机编队的模型预测控制算法

针对多无人机在编队飞行过程中需满足机间避碰、通信、避障等约束的问题,设计一种考虑多约束的分布式模型预测控制算法,使无人机编队在满足上述约束的前提下,实现轨迹跟踪、队形保持.首先,在不考虑通信时延、外界干扰、噪声的情况下,以四旋翼为控制对象,建立线性时不变的单机及编队运动模型;然后,在考虑状态约束、输入约束、机间避碰、机间通信、避障等多种约束的情况下,以轨迹跟踪、队形保持为控制目标,基于虚拟领航策略设计一种分布式模型预测控制算法;接着,对优化问题的可行性以及编队系统的渐近稳定性进行分析,其中算法的终端部分设计、相容性约束设计是保证系统稳定的关键;最后,利用6架无人机仿真验证所提出控制算法的有效性.     

Safe Autonomous Agent Formation Operations Via Obstacle Collision Avoidance

In this paper, we consider the problem of flocking and shape‐orientation control of multi‐agent systems with inter‐agent and obstacle collision avoidance. We first consider the problem of forcing a set of autonomous agents to form a desired formation shape and orientation while avoiding inter‐agent collision and collision with convex obstacles, and following a trajectory known to only one of the agents, namely the leader of the formation. Then we build upon the solution given to this problem and solve the problem of guaranteeing obstacle collision avoidance by changing the size and the orientation of the formation. Changing the size and the orientation of the formation is helpful when the agents want to go through a narrow passage while the existing size or orientation of the formation does not allow this. We also propose collision avoidance algorithms that temporarily change the shape of the formation to avoid collision with stationary or moving nonconvex obstacles. Simulation results are presented to show the performance of the proposed control laws.     

Application of distributed predictive control to motion and coordination problems for unicycle autonomous robots

This paper presents a Distributed Predictive Control (DPC) approach for the solution of a number of motion and coordination problems for autonomous robots. The proposed scheme is characterized by a multilayer structure: at the higher layer the reference trajectories of the robots are computed as the solution of suitable optimization problems. It is shown that, at this level, the definition of the cost function to be minimized allows to consider many different problems, such as formation control, coverage and optimal sensing, containment control, inter-robot and obstacle collision avoidance, and patrolling in an unknown environment. At the lower layers of the control structure, proper state and control reference trajectories are defined and a robust Model Predictive Control (MPC) problem is solved by each robot. To reduce the computational burden required by the algorithm, collision and obstacle avoidance constraints are reformulated in linear terms, so that the optimization problem to be solved on-line is a Quadratic Programming (QP) one. A number of experimental and simulation results are reported to witness the flexibility and performances of the method.     

A Scalable Adaptive Approach to Multi-Vehicle Formation Control with Obstacle Avoidance

This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multi-vehicle systems (MVSs) in complex obstacle-laden environments. The MVS under consideration consists of a leader vehicle with an unknown control input and a group of follower vehicles, connected via a directed interaction topology, subject to simultaneous unknown heterogeneous nonlinearities and external disturbances. The central aim is to achieve effective and collision-free formation tracking control for the nonlinear and uncertain MVS with obstacles encountered in formation maneuvering, while not demanding global information of the interaction topology. Toward this goal, a radial basis function neural network is used to model the unknown nonlinearity of vehicle dynamics in each vehicle and repulsive potentials are employed for obstacle avoidance. Furthermore, a scalable distributed adaptive formation tracking control protocol with a built-in obstacle avoidance mechanism is developed. It is proved that, with the proposed protocol, the resulting formation tracking errors are uniformly ultimately bounded and obstacle collision avoidance is guaranteed. Comprehensive simulation results are elaborated to substantiate the effectiveness and the promising collision avoidance performance of the proposed scalable adaptive formation control approach.      

Control of cooperative manipulator-endowed systems under high-level tasks and uncertain dynamics

This paper considers the problem of distributed motion- and task-planning of multi-agent and multi-agent-object systems under temporal-logic-based tasks and uncertain dynamics. We focus on manipulator-endowed robotic agents that can interact with their surroundings. We present first continuous control algorithms for multi-agent navigation and cooperative object manipulation that exhibit the following properties. First, they are distributed in the sense that each agent calculates its own control signal from local interaction with the other agents and the environment. Second, they guarantee safety properties in terms of inter-agent collision avoidance and obstacle avoidance. Third, they adapt on-the-fly to dynamic uncertainties and are robust to exogenous disturbances. The aforementioned algorithms allow the abstraction of the underlying system to a finite-state representation. Inspired by formal-verification techniques, we use such a representation to derive plans for the agents that satisfy the given temporal-logic tasks. Various simulation results and hardware experiments verify the efficiency of the proposed algorithms.     

A 3D reactive collision avoidance algorithm for underactuated underwater vehicles

Avoiding collisions is an essential goal of the control system of autonomous vehicles. This paper presents a reactive algorithm for avoiding obstacles in a three‐dimensional space, and shows how the algorithm can be applied to an underactuated underwater vehicle. The algorithm is based on maintaining a constant avoidance angle to the obstacle, which ensures that a guaranteed minimum separation distance is achieved. The algorithm can thus be implemented without knowledge of the obstacle shape. The avoidance angle is designed to compensate for obstacle movement, and the flexibility of operating in 3D can be utilized to implement traffic rules or operational constraints. We exemplify this by incorporating safety constraints on the vehicle pitch and by making the vehicle seek to move behind the obstacle, while also minimizing the required control effort. The underactuation of the vehicle induces a sway and heave movement while turning. To avoid uncontrolled gliding into the obstacle, we account for this movement using a Flow frame controller, which controls the direction of the vehicle's velocity rather than just the pitch and yaw. We derive conditions under which it is ensured that the resulting maneuver is safe, and these results are verified trough simulations and through full‐scale experiments on the Hugin HUS autonomous underwater vehicle. The latter demonstrates the performance of the proposed algorithm when applied to a case with unmodeled disturbances and sensor noise, and shows how the modular nature of the collision avoidance algorithm allows it to be applied on top of a commercial control system.     

A Distributed Deployment Strategy for Multi-Agent Systems Subject to Health Degradation and Communication Delays

This paper presents a distributed deployment algorithm for a network of autonomous agents. The main goal is to perform a coverage task when the sensor effectiveness of each agent varies during the mission. It is also assumed that the agents are subject to communication delays induced by communication faults. To improve the overall performance and guarantee the collision avoidance, the guaranteed multiplicatively-weighted Voronoi (GMW-Voronoi) diagram is introduced. A distributed coverage control is then provided to drive agents in such a way that the coverage performance function is minimized over the regions assigned to agents. The effectiveness of the proposed algorithm is evaluated by simulations.     

一种用于群体模拟的分层次避障法

个体避障是实现基于主体的(agent-based)群体模拟中一个很重要的问题,为了实现个体间以及个体和环境间的碰撞避免并杜绝穿透,人们提出了大量避障方法.但是,这些方法面临的挑战在于:如何杜绝穿透现象并最大程度地减少由于避障需求而带来的个体行为模拟上的空间限制和失真.针对这一问题,提出了一种分层次避障方法,从静态避障、动态避障、穿透矫正3个不同的层次对避障进行处理.静态避障层和动态避障层通过对物体的划分和分别避障,极大地减少了各层次避障时需要考虑的各种复杂情形;而基于可变包围盒和原位置的穿透矫正层则有效地杜绝了模拟中出现的穿透现象,也消除了现有模拟中由于避免穿透而引入的空间限制和失真.     

固定翼无人机在线航迹规划方法

在不确定环境下,针对固定翼无人机（UAV）航迹规划问题,提出了一种基于滚动时域控制的模糊粒子群优化算法与改进人工势场法相结合的在线航迹规划方法。首先,对凸多边形障碍物进行最小外接圆拟合;然后,根据静态威胁,将规划问题转化为一系列时域窗口内的在线子问题,利用模糊粒子群算法实时优化求解以实现静态避障;当环境中存在动态威胁时,使用改进人工势场法对航迹进行调整完成动态避障。为了满足固定翼无人机的动态约束,同时提出固定翼UAV的碰撞检测法,可提前判断障碍物是否为真正威胁源,以此减少转弯频率和幅度,降低飞行代价。仿真实验结果表明,所提方法在固定翼UAV航迹规划中能有效提升规划速度、稳定性与实时避障能力,且克服了传统人工势场容易陷入局部最优的缺点。     

On the use of mixed‐integer linear programming for predictive control with avoidance constraints

This technical note concerns the predictive control of discrete‐time linear models subject to state, input and avoidance polyhedral constraints. Owing to the presence of avoidance constraints, the optimization associated with the predictive control law is non‐convex, even though the constraints themselves are convex. The inclusion of the avoidance constraints in the predictive control law is achieved by the use of a modified version of a mixed‐integer programming approach previously derived in the literature. The proposed modification consists of adding constraints to ensure that linear segments of the system trajectories between consecutive sampling times do not cross existing obstacles. This avoids the significant extra computation that would be incurred if the sampling time was reduced to prevent these crossings. Simulation results show that the inclusion of these additional constraints successfully prevents obstacle collisions that would otherwise occur. Copyright © 2008 John Wiley & Sons, Ltd.     

Networked model predictive control based on neighbourhood optimization for serially connected large-scale processes

In this paper, two novel networked model predictive control schemes based on neighbourhood optimization are presented for on-line optimization and control of a class of serially connected processes (known as the cascade processes in some references), in which the on-line optimization of the whole system is decomposed into that of several small-scale subsystems in distributed structures. Under network environment, the connectivity of the communication network is assumed to be sufficient for each subsystem to exchange information with its neighbour subsystems. An iterative algorithm for networked MPC and a networked MPC algorithm with one-step delay communication are developed according to different network capacities. The optimality of the iteration based networked MPC algorithm is analyzed and the nominal stability is derived for unconstrained distributed control systems. The nominal stability with one-step delay communication is employed for distributed control systems without the inequality constraints. Finally, an illustrative example and the simulation study of the fuel feed flow control for the walking beam reheating furnace are provided to test the effectiveness and practicality of the proposed networked MPC algorithms.     

基于Leader-Follower的分队队形控制寻径算法研究

在战场环境中,战术分队的队形在面对复杂静态或动态障碍物难以较好地保持,针对此问题,提出了基于Leader-Follower算法的改进队形控制方法。在Leader寻径阶段,通过在战场导航网格中应用两阶段路径搜索方法,先使用A*算法寻找由三角形通道和可利用地物组成的路径,再使用改进的Funnel算法在考虑队形规模的约束条件下对路径作平滑处理。在Follower跟随阶段中,通过采用morphing技术,产生在复杂障碍约束下平滑的中间约束队形序列,并结合提出的队形弹簧模型,局部修正并控制Follower每一时刻的速度。为解决面对动态障碍的避碰问题,基于相对速度障碍法,并加入速度协同控制,避免队形在避碰过程中失效。最后通过实验表明了该方法的有效性。     

Formation and obstacle avoidance control for multiagent systems

This paper considers the problems of formation and obstacle avoidance for multiagent systems.The objective is to design a term of agents that can reach a desired formation while avoiding collision with obstacles.To reduce the amount of information interaction between agents and target,we adopt the leader-follower formation strategy.By using the receding horizon control (RHC),an optimal problem is formulated in terms of cost minimization under constraints.Information on obstacles is incorporated online as sensed in a limited sensing range.The communication requirements between agents are that the followers should obtain the previous optimal control trajectory of the leader to each update time.The stability is guaranteed by adding a terminal-state penalty to the cost function and a terminal-state region to optimal problem.Finally,simulation studies are provided to verify the effectiveness of the proposed approach.     

三维动态环境下多无人机编队分布式保持控制

针对无人机编队沿参考轨迹飞行时遭遇突发障碍物而发生碰撞的问题, 提出一种可实时避障及机间避碰的分布式编队保持算法. 基于虚拟结构编队策略, 采用非线性模型预测控制(NMPC) 方法设计分布式编队控制器. 为了实现通讯延迟下的机间避碰, 采用基于不同优先级的改进避碰惩罚策略. 仿真结果表明, 所设计的分布式编队控制器能保证编队及时避开环境中的突发障碍物, 且无人机间不发生互碰, 避障后的各编队继续以原队形沿参考轨迹飞行.

     

Target tracking and obstacle avoidance for multi-agent systems

This paper considers the problems of target tracking and obstacle avoidance for multi-agent systems. To solve the problem that multiple agents cannot effectively track the target while avoiding obstacle in dynamic environment, a novel control algorithm based on potential function and behavior rules is proposed. Meanwhile, the interactions among agents are also considered. According to the state whether an agent is within the area of its neighbors' influence, two kinds of potential functions are presented. Meanwhile, the distributed control input of each agent is determined by relative velocities as well as relative positions among agents, target and obstacle. The maximum linear speed of the agents is also discussed. Finally, simulation studies are given to demonstrate the performance of the proposed algorithm.     

融合动态障碍物运动信息的路径规划算法

传统的路径规划算法只能在障碍物不发生位置变化的环境中计算最优路径。但是随着机器人在商场、医院、银行等动态环境下的普及,传统的路径规划算法容易与动态障碍物发生碰撞等危险。因此,关于随机动态障碍物条件下的机器人路径规划算法需要得到进一步改善。为了解决在动态环境下的机器人路径规划问题,提出了一种融合机器人与障碍物运动信息的改进动态窗口法来解决机器人在动态环境下的局部路径规划问题,并且与优化A*算法相结合来实现全局最优路径规划。主要内容体现为：在全局路径规划上,采用优化A*算法求解最优路径。在局部路径规划上,以动态障碍物的速度作为先验信息,通过对传统动态窗口法的评价函数进行扩展,实现机器人在动态环境下的自主智能避障。实验证明,该算法可以实现基于全局最优路径的实时动态避障,具体表现为可以在不干涉动态障碍物的条件下减少碰撞风险、做出智能避障且路径更加平滑、长度更短、行驶速度更快。     

Robust distributed model predictive control

This paper presents a formulation for distributed model predictive control (DMPC) of systems with coupled constraints. The approach divides the single large planning optimization into smaller sub-problems, each planning only for the controls of a particular subsystem. Relevant plan data is communicated between sub-problems to ensure that all decisions satisfy the coupled constraints. The new algorithm guarantees that all optimizations remain feasible, that the coupled constraints will be satisfied, and that each subsystem will converge to its target, despite the action of unknown but bounded disturbances. Simulation results are presented showing that the new algorithm offers significant reductions in computation time for only a small degradation in performance in comparison with centralized MPC.