Fast path planning using experience learning from obstacle patterns

We consider the problem of robot path planning in an environment where the location and geometry of obstacles are initially unknown while reusing relevant knowledge about collision avoidance learned from robots' previous navigational experience. Our main hypothesis in this paper is that the path planning times for a robot can be reduced if it can refer to previous maneuvers it used to avoid collisions with obstacles during earlier missions, and adapt that information to avoid obstacles during its current navigation. To verify this hypothesis, we propose an algorithm called LearnerRRT that first uses a feature matching algorithm called Sample Consensus Initial Alignment (SAC-IA) to efficiently match currently encountered obstacle features with past obstacle features, and, then uses an experience based learning technique to adapt previously recorded robot obstacle avoidance trajectories corresponding to the matched feature, to the current scenario. The feature matching and machine learning techniques are integrated into the robot's path planner so that the robot can rapidly and seamlessly update its path to circumvent an obstacle it encounters, in real-time, and continue to move towards its goal. We have conducted several experiments using a simulated Coroware Corobot robot within the Webots simulator to verify the performance of our proposed algorithm, with different start and goal locations, and different obstacle geometries and placements, as well as compared our approach to a state-of-the-art sampling-based path planner. Our results show that the proposed algorithm LearnerRRT performs much better than Informed RRT^∗. When given the same time, our algorithm finished its task successfully whereas Informed RRT^∗ could only achieve 10 - 20 percent of the optimal distance.