中文摘要：

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．

英文摘要：

By considering the barrier-following motion and collision avoidance, a novel search method based on a simpli- fied virtual-force model is proposed for multi-target search of swarm robots （SRSMT-SVF） in unknown environments with non-convex, convex and dynamic obstacles. The multi-target search behaviour of swarm robots in complicated environments is firstly decomposed, and a simplified virtual-force model is then formulated. Based on the proposed model, the motion con- trol strategies of the individual robots under coordinated search and roaming state are designed to achieve real-time collision avoidance in searching process. Simulation results on different scale of swarm robot systems demonstrate that the proposed method can keep the individual robot with a good performance of collision avoidance, and can effectively reduce the colli- sion conflicts between the robots and the environment as well as collision conflicts among the individual robots during the searching process. Moreover, compared with the extended particle swarm optimization （EPSO）, the search time and energy consumption are reduced by at least 13.78% and 11.96%. The numeric simulation results demonstrate its effectiveness.