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The probabilistic roadmap planning has emerged as a powerful framework for path planning of mobile robots due to its computational efficiency, implementation simplicity, and scalability in different problems. This planning method proceeds in two phases. In the construction phase a roadmap is incrementally constructed and stored as a graph G whose nodes are free configurations sampled on the robot’s configuration space and whose edges correspond to collision-free paths between these configurations. In the query phase, given any start and goal configurations, α and β respectively, the planner first connects α and β to G by adding edges that correspond to collision-free paths, and then searches for a path in G between α and β. In this work, we address mainly the roadmap construction phase. The goal here is to provide a comparative analysis of a number of combinations of different techniques for sampling free configurations and different node adding techniques, all implemented in a single system and applied to the same test workspace. Results help probabilistic roadmap planning users to choose the best combination for their applications.

Análise de técnicas para amostragem e seleção de vértices no planejamento probabilístico de mapa de rotas.