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针对未知凸和非凸障碍物以及动态障碍物环境下群机器人多目标搜索问题,提出了一种基于简化虚拟受力分析模型的循障和避碰方法(SRSMT-SVF).对复杂环境下群机器人多目标搜索行为进行了分解并抽象出简化虚拟受力分析模型.基于此受力模型,设计了个体机器人协同搜索和漫游状态下的运动控制策略,使得机器人在搜索目标的同时能够实时避碰.通过对不同群体规模系统的仿真实验表明,本文控制方法能够使个体机器人在整个搜索过程中保持良好的避碰性能,有效地减少系统与环境之间和系统内部个体之间的碰撞冲突.相比于扩展粒子群算法(EPSO),本文方法使得搜索耗时和系统能耗至少减少了13.78%、11.96%,数值仿真结果验证了本文方法的有效性.
Aiming at the problem of multiobjective search of group robots with unknown convex and non-convex obstacles and dynamic obstacles, a method of obstacle avoidance and collision avoidance (SRSMT-SVF) based on simplified virtual force analysis model is proposed. The robot multi-objective search behavior is decomposed and abstracted to simplify the simplified model of virtual force analysis.Based on this model, a collaborative search of individual robots and a motion control strategy in the roaming state are designed so that the robot can avoid collision in real time while searching for targets According to the simulation experiment of different groups’ size system, the control method of this paper can make the individual robot keep the good collision avoidance performance throughout the search process and effectively reduce the collisions between the system and the environment and between individuals in the system. Compared with Extended Particle Swarm Optimization (EPSO), the proposed method reduces search time and system energy consumption by at least 13.78% and 11.96% respectively. Numerical simulation results show the effectiveness of the proposed method.