Fast Path Planning using Experience Learning from Obstacle Patterns
Advisor Information
Prithviraj Dasgupta
Location
UNO Criss Library, Room 225
Presentation Type
Oral Presentation
Start Date
4-3-2016 11:00 AM
End Date
4-3-2016 11:15 AM
Abstract
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 research 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 in real-time and continue its navigation 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 as well as compared our approach to a 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.
Fast Path Planning using Experience Learning from Obstacle Patterns
UNO Criss Library, Room 225
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 research 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 in real-time and continue its navigation 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 as well as compared our approach to a 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.