A Grid-Based Motion Planning Approach for Coherent Groups

This paper presents a novel motion planning approach for coherent groups with constant area, and it integrates C-L method into the probabilistic roadmap algorithm with sampling on the medial axis (MAPRM). In the preprocessing phase, the group is discretized into a grid-set which represents the configuration of the group. Then, a number of samples are generated on workspace by medial axis technique. These samples are extended into group’s configuration nodes of the roadmap using an extending strategy. Also, the group's deformation degree relative to the desired shape is introduced to improve the evaluation function. It gives users more flexibility to determine the respective weights of the group’s deformation degree and its distance to the goal in the query phase. After that, a novel local planner is constructed to connect any two neighbor configurations by using C-L method and the improved evaluation function. Experiments show that our approach is able to find paths for the coherent group efficiently and keep its area invariant when moving toward the goal.