Cooperative co-evolution based distributed path planning of multiple mobile robots

This paper proposes novel multiple-mobile-robot collision avoidance path planning based on cooperative co-evolution, which can be executed fully distributed and in parallel. A real valued co-evolutionary algorithm is developed to coordinate the movement of multiple robots in 2D world, avoiding C-space or grid net searching. The collision avoidance is achieved by cooperatively co-evolving segments of paths and the time interval to pass them. Methods for constraint handling, which are developed for evolutionary algorithm, make the path planning easier. The effectiveness of the algorithm is demonstrated on a number of 2D path planning problems.     

Neural network and genetic algorithm based global path planning in a static environment

INTRODUCTION The path planning problem of a mobile robot is to find a safe and efficient path for the robot, given a start location, a goal location and a set of obstacles distributed in a workspace. The robot can go from the start location to the goal location without colliding with any obstacle along the path. In addition to the fundamental problem, we also try to find a way to optimize the plan, say to minimize the time required or distance traveled (Wu et al., 1996; Sadati and Ta-he…     

Weighted time-based global hierarchical path planning in dynamic environment

A weighted time-based global hierarchical path planning method is proposed to obtain the global optimal path from the starting point to the destination with time optimal control. First, the grid- or graph-based modeling is performed and the environment is divided into a set of grids or nodes. Then two time-based features of time interval and time cost are presented. The time intervals for each grid are built, during each interval the condition of the grid remains stable, and a time cost of passing through the grid is defined and assigned to each interval. Furthermore, the weight is introduced for taking both time and distance into consideration, and thus a sequence ofmultiscale paths with total time cost can be achieved. Experimental results show that the proposed method can handle the complex dynamic environment, obtain the global time optimal path and has the potential to be applied to the autonomous robot naviga- tion and traffic environment.     

智能机器人综合路径规划算法在Matlab中的实现

在充分考虑了运动障碍物随机性的基础上，研究了随机运动障碍物的位置分布规律，并结合随机运动障碍物具有惯性的特点，提出了对机器人运动路线进行逐步判别的一种综合路径规划方法。仿真结果表明，该算法正确、高效、实用，对机器人绕过随机运动障碍物具有一定的参考价值。