动态环境下多机器人合作追捕研究

主要研究了多个自主型移动机器人合作追捕多个运动猎物的追捕—逃避问题. 从基于范例的推理缩小任务招标范围、引入辅助决策矩阵改善联盟决策两个方面对传统的合同网协议进行了改进，以减少任务协商过程中的通信开销，接着引入了联盟生命值、违约金等概念，在此基础上，实现了一种允许动态联盟的多机器人合作追捕算法. 仿真实验证明了所提算法的可行性和有效性.  

Geometry of Pursuit-Evasion on Second Order Rotation Surfaces

Differential games with simple motionin a game space with non-unique shortest geodesic line havephase portraits with complicated structure of singular surfaces.The successful solution of such games on a family of two-dimensionalcones [1] was based to the large extent on the parameter analysisof the problem. In the present paper the results of pursuit gameon a cone of one sheet are extended to the game on the full two-sheetcone. The latter surface is included in one-parametric familyof rotation surfaces (each of which is characterized by two moreparameters), and the game on it is considered as the generatingproblem for the analysis of the games on the perturbed surfaces.The paper continues the previous investigations of the authors.In [2], [3] some local results are obtained for the games onRiemannian manifolds. In [1], [2] the game problems are solvedfor a two-dimensional cone, in [4], [5] one sheet of a double-sheetedrotation hyperboloid is considered as game space and geometricalproperties of trajectories are analyzed.  

A cooperative pursuit-evasion game in wireless sensor and actor networks

This paper studies the problem of the pursuit-evasion game under the wireless sensor and actor networks (WSANs). In order to plan paths for pursuers to capture an evader in the pursuit-evasion game, a novel multi-step cooperative strategy is presented. Under this strategy, the pursuit-evasion game is studied in two stages. In the first stage we assume that the evader is always static in the workplace, and in the second stage the evader will move once it senses the existence of pursuers. A Daisy-Chain Formation algorithm and a sliding mode-based method are presented to control the pursuit. Based on Lyapunov stability theory, the proposed algorithm is proved to be convergent. At last, simulation results are provided to demonstrate the effectiveness of the proposed method.  

A cooperative Homicidal Chauffeur game

We address a pursuit-evasion problem involving an unbounded planar environment, a single evader and multiple pursuers moving along curves of bounded curvature. The problem amounts to a multi-agent version of the classic Homicidal Chauffeur problem; we identify parameter ranges in which a single pursuer is not sufficient to guarantee evader capture. We propose a novel multi-phase cooperative strategy in which the pursuers move in specific formations and confine the evader to a bounded region. The proposed strategy is inspired by the hunting and foraging behaviors of various fish species. We characterize the required number of pursuers for which our strategy is guaranteed to lead to confinement.  

基于协同进化的平面追逃对策研究

建立了二维平面内动力学约束下迫逃运动的数学模型.首先为追捕者设计了基于比例制导算法和进化算法的混合追捕策略,以提高其追捕能力;然后利用协同进化算法对追捕者和逃跑者的追选策略进行进化.仿真结果表明,进化后的逃跑策略能有效规避比例制导的追捕者.逃跑者在协同进化过程中涌现出众多复杂多变的规避策略.  

The role of information in the cop-robber game

We investigate the role of the information available to the players on the outcome of the cops and robbers game. This game takes place on a graph and players move along the edges in turns. The cops win the game if they can move onto the robber’s vertex. In the standard formulation, it is assumed that the players can “see” each other at all times. A graph G$G$ is called cop-win if a single cop can capture the robber on G$G$. We study the effect of reducing the cop’s visibility. On the positive side, with a simple argument, we show that a cop with small or no visibility can capture the robber on any cop-win graph (even if the robber still has global visibility). On the negative side, we show that the reduction in cop’s visibility can result in an exponential increase in the capture time. Finally, we start the investigation of the variant where the visibility powers of the two players are symmetrical. We show that the cop can establish eye contact with the robber on any graph and present a sufficient condition for capture. In establishing this condition, we present a characterization of graphs on which a natural greedy pursuit strategy suffices for capturing the robber.  

On vehicle placement to intercept moving targets

We address optimal placement of vehicles with simple motion to intercept a mobile target that is generated stochastically on a line segment. The optimality of vehicle placement is measured through a cost function associated with intercepting the target. With a single vehicle, we assume that the target moves (i) with fixed speed and in a fixed direction perpendicular to the line segment, or (ii) to maximize the distance from the line segment, or (iii) to maximize intercept time. In each case, we show that the cost function is strictly convex, its gradient is smooth, and the optimal vehicle placement is obtained by a standard gradient-based optimization technique. With multiple vehicles, we assume that the target moves with fixed speed and in a fixed direction perpendicular to the line segment. We present a discrete-time partitioning and gradient-based algorithm, and characterize conditions under which the algorithm asymptotically leads the vehicles to a set of critical configurations of the cost function.  

近地共面轨道上两飞行器在径向连续小推力下的追逃界栅

针对近地共面轨道上两飞行器在轨追逃对策问题,以轨道根数为状态变量,在双方均为径向连续可变小推力的假设条件下,研究定性微分对策方法中考虑推力性能的界栅存在条件.在此条件下推导出可变推力的最优控制量,以及以偏心率和幅角为状态变量的界栅表达式.仿真实例在给定捕获半径的条件下,给出了偏心率的界栅曲线,并表明当对策结束时,逃逸器位于追踪器的正上方或正下方.  

复杂三维多面体环境中空地协作追逃问题

结合无人机(UAV)的空中移动和无人车(UGV)的地面移动特点, 本文提出了一种UAV/UGV空地协作系统, 并且针对其在复杂地形中的追逃问题, 提出了一种复杂三维多面体环境中UAV/UGV空地协作追逃策略. 首先介绍 了UAV/UGV空地协作系统的结构与协作追逃问题描述. 接着将边界值问题(BVP)改进并离散化作为博弈走法生成 器. 然后, 针对逃方已知追方位置, 而追方只具备直线视野(LOS)的不利条件, 分析了最坏情况. 逃方策略在保证最 大生存条件下尽可能获得博弈胜利. 追方策略根据逃方状态分成3种情况进行讨论: 逃方处于追方的视野范围内、 逃方刚刚消失于追方视野以及追方完全丢失逃方的情况. 最后, 对比仿真结果说明了本文算法的有效性, 并分析了 追逃结果的影响因素. 由于地形是非凸的并且充满障碍, 因此该策略虽不能保证追方一定能够胜利, 但在最坏情况 下是最优的.  

A model-based approximate λ-policy iteration approach to online evasive path planning and the video game Ms. Pac-Man

This paper presents a model-based approximate λ-policy iteration approach using temporal differences for optimizing paths online for a pursuit-evasion problem, where an agent must visit several target positions within a region of interest while simultaneously avoiding one or more actively pursuing adversaries. This method is relevant to applications, such as robotic path planning, mobile-sensor applications, and path exposure. The methodology described utilizes cell decomposition to construct a decision tree and implements a temporal difference-based approximate λ-policy iteration to combine online learning with prior knowledge through modeling to achieve the objectives of minimizing the risk of being caught by an adversary and maximizing a reward associated with visiting target locations. Online learning and frequent decision tree updates allow the algorithm to quickly adapt to unexpected movements by the adversaries or dynamic environments. The approach is illustrated through a modified version of the video game Ms. Pac-Man, which is shown to be a benchmark example of the pursuit-evasion problem. The results show that the approach presented in this paper outperforms several other methods as well as most human players.