We consider the problem of robot path planning in an initially unknown environment where the robot does not have access to an a priori map of its environment but is aware of some common obstacle patterns along with the paths that enable it to circumnavigate around these obstacles. In order to autonomously improve its navigation performance, the robot should be able to identify significant obstacle patterns and learn corresponding obstacle avoidance maneuvers as it navigates through different environments in order to solve its tasks. To achieve this objective, we propose a novel online algorithm called Incremental State Discovery Via Clustering (ISDC) which enables a robot to dynamically determine important obstacle patterns in its environments and their best representations as combinations of initially available basic obstacle patterns. Our results show that ISDC, when combined with our previously proposed navigation technique, was able to identify significant obstacle patterns in different environments in a time effective manner which accelerated the overall path planning and navigation times for the robots.
We present a novel optimization-based algorithm for motion planning in dynamic environments. Our approach uses a stochastic trajectory optimization framework to avoid collisions and satisfy smoothness and dynamics constraints. Our algorithm does not require a priori knowledge about global motion or trajectories of dynamic obstacles. Rather, we compute a conservative local bound on the position or trajectory of each obstacle over a short time and use the bound to compute a collision-free trajectory for the robot in an incremental manner. Moreover, we interleave planning and execution of the robot in an adaptive manner to balance between the planning horizon and responsiveness to obstacle. We highlight the performance of our planner in a simulated dynamic environment with the 7-DOF PR2 robot arm and dynamic obstacles.
This paper suggests and supports a design idea for improving dynamical navigation: adding an intermediary, adaptive obstacle representation level between perception and repeller representations. We illustrate our idea with our specific example of an adaptive obstacle representation level, which cleanly integrates into multiple existing navigation systems, treating each perceived obstacle entity as a locally sensitive, obstacle-valued function that returns an obstacle representation upon which steering and obstacle avoidance are based. Moreover, other elements of the navigation systems remain unaltered, thus preserving and extending original design virtues such as behavioral flexibility, computational efficiency, and dynamic responsiveness. Extensive simulations, validated with tests of real robots, demonstrate that our new representations compare favorably to previously employed representations on measures of effectiveness within a tested scenario, robustness over varying scenarios and ranges of parameter values, and computational efficiency.
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 ConsensusInitial 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 InformedRRT*. When given the same time, our algorithm finished its task successfully whereas Informed RRT* could only achieve 10-20 percent of the optimal distance.