中文摘要：

机器人学是现在及未来科技发展的重点，路径规划是机器人学中的一个重要课题．生物界一些群居动物有严格的等级制度和职责分工，受社会群居动物行为启发，提出社会群体搜索算法（socialgroupsearchalgorithm，SGS0）．社会群体搜索算法对群体的分类及信息反馈机制——领导一追随机制的制定，降低了早熟的概率，交叉变异和淘汰机制的引入增加了搜索范围，减少了陷入局部最优的可能．同时，对提出的社会群体搜索算法进行了分析，从理论上证明了算法的收敛性；将社会群体搜索算法应用于机器人路径规划进行仿真，从实验中验证了算法的有效性，并与遗传算法和粒子群算法比较，进一步证明了社会群体搜索算法在机器人路径规划问题中的有效性和高效性．

英文摘要：

With the rapid development of technology, ＂A robot in every home＂ will come true in the near future. Path planning for mobile robots, as an important object of robotics, has drawn amount of attentions. Inspired by the strict hierarchy and division of responsibilities by social group of animals, social group search algorithm is proposed in this paper. Individuals are classified into four groups with different search strategies according to leader-follower model. When the individuals are searching for optimal object, head leader is the best candidate and searching for better position along with leaders, which are the subordinates with well performance. Followers find the proper position by following the leaders and searching better one around them. Meantime, dispersed numbers are the also-runs which perform the worst and should be replaced by new-borns. Leaders and followers are responsible for searching optimal position, while dispersed numbers ensure the algorithm beyond the local optimum. Furthermore, crossover and mutation, as well as elimination mechanisms are introduced to our algorithm to enlarge the search scope. As a result, comparing with genetic algorithm and particle swarm optimizer, the possibility of premature and local optimum is reduced. The convergence is verified mathematically and experimentally. Via numerous simulations and comparison with other classical algorithms, the characters of high efficiency and effectiveness for path planning problem are illustrated, which are especially of great significance for the further research in robotic navigation.