Incremental Adaptive Probabilistic Roadmaps for Mobile Robot Navigation under Uncertain Condition

As the application domains of sampling-based motion planning grow, more complicated planning problems arise that challenge the functionality of these planners. One of the main challenges is the weak performance when reacting to uncertainty in robot motion, obstacles, and sensing. In this paper, a multi-query sampling-based planner is presented based on the optimal probabilistic roadmaps algorithm that employs a hybrid sample classification and self-adjustment strategy to handle diverse types of planning uncertainty. The proposed method starts by storing the collision-free generated samples in a matrix-grid structure. Using the resulted grid structure makes it computationally cheap to search and find samples in a specific region. As soon as the robot senses an obstacle during the execution of the initial plan, the occupied grid cells are detected, relevant samples are selected, and in-collision vertices are removed within the vision range of the robot. Furthermore, a second layer of nodes connected to the current direct neighbors are checked against collision which gives the planner more time to react to uncertainty before getting too close to an obstacle. The simulation results in problems with uncertainty show significant improvement comparing to similar algorithms in terms of failure rate, processing time and minimum distance from obstacles. The planner was also successfully implemented on a TurtleBot in two different scenarios with uncertainty.