基于鸽群优化算法的实时避障算法

为保证机器人能安全无碰撞地抵达目标位置,提出一种在改进版圆形扩张（CSE+）法中融合鸽群优化算法的实时避障算法。所提算法引入对障碍物密集程度的判断机制,在障碍分布密集时选择最安全的路径,在障碍物分布稀松的环境中,利用鸽群优化算法在安全范围内寻找下一目标最优位置。此外,还引入了搜索树,可实现死角的检测与避免。仿真结果显示:所提避障算法能提高路径规划的性能,在障碍物分布稀松时效果更加明显,且可实现死角检测并能通过狭长通道。 

Real-time obstacle avoidance algorithm based on pigeon-inspired optimization

In order to ensure that the mobile robot can reach the target position without collisions, this paper proposes a real-time obstacle avoidance algorithm that integrates the pigeon-inspired optimization into the Circle Sector Expansion plus (CSE+) method. This algorithm includes a judgment mechanism to evaluate the distribution of obstacles. When the obstacles are densely distributed, the safest path will be selected. Otherwise, the pigeon-inspired optimization will be used to find an optimal position as the next target position in the safe range. In addition, a search tree is used to detect and avoid the dead-end situation. The simulation results show that this algorithm can improve the efficiency of path planning, the effect is more obvious when the obstacles are sparsely distributed, the dead-end situation can be detected, and the robot can pass through the narrow and long corridors.