未知环境下群机器人多目标搜索协同控制

Multi-target search of swarm robots cooperative control in an unknown environment

中文关键词  群机器人学  群体智能  多目标搜索  边界扫描避障策略  目标位置估计  粒子群算法

英文关键词  swarm robotics  swarm intelligence  multi-target search  boundary scan obstacle avoidance strategy  target position estimation  particle swarm optimization (PSO)

中文摘要

      未知环境下, 群机器人无法预先获取多目标搜索的环境信息, 仅可局部感知与局部通信. 本文针对避障效 率与搜索效率的缺陷提出边界扫描的避障策略和目标位置估计的粒子群算法, 边界扫描的避障策略(BSOA)将障碍 物简化成连续障碍物与非连续障碍物两种情况, 并根据情况向特定边界运动; 目标位置估计的粒子群算法 (TPEPSO)则利用获取的目标信号估计目标位置, 结合粒子群算法到达目标附近, 从而实现目标搜索. 提出的方法与 基于简化虚拟受力分析模型的循障避碰方法(SVF)及扩展粒子群算法(EPSO)、自适应机器人蝙蝠算法(ARBA)仿真 比较, 搜索效率提高5.72% ～ 21.58%, 总能耗减少4.30% ～ 19.11%.

英文摘要

      In an unknown environment, the swarm robots have no access to obtain the environment information of multi-target search in advance, and their capabilities of sense and communication are limited. Aiming at improving the efficiency of multi-target search and obstacle avoidance, a boundary scan obstacle avoidance strategy (BSOA) and a target position estimation particle swarm optimization (TPEPSO) are proposed in this paper. The former simplifies obstacles into two types: continuous obstacles and discontinuous obstacles, and provides useful guides for the robots to move towards different boundaries based on specific situation. The latter is applied to estimate the target positions with the accessible signals, then the combination of the received information and the PSO helps to approach the targets successfully, which is the realization of target search. As simulated in this paper, the searching efficiency has been increased by 5.72% ～ 21.58%, and the total energy consumption has been reduced by 4.30% ～ 19.11%, according to the comprehensively comparison between the two and three other methods: the simplified virtual-force obstacle avoidance method (SVF), the extended particle swarm optimization (EPSO) and the adaptive robot bat algorithm (ARBA).