Metareasoning Strategies to Correct Navigation Failures of Autonomous Ground Robots

Abstract

Due to the complexity of autonomous systems, theoretically perfect path planning algorithms sometimes fail due to emergent behaviors that arise when interacting with different perception, mapping and goal planning subprocesses. These failures prevent mission success, especially in complex environments that have not previously been explored by the robot. To overcome these failures, many researchers have sought to develop parameter learning methods to improve either mission success or path planning convergence. Metareasoning, which can be simply described as “thinking about thinking,” offers another possible solution for mitigating these planning failures. This project offers a novel metareasoning approach that uses different methods of monitoring and control to detect and overcome path planning irregularities that contribute to path planning failures. All methods for the approaches were implemented as a part of the ARL ground autonomy stack which uses both global and local path planning ROS nodes. The proposed monitoring methods include listening to messages published to the system by the planning algorithms themselves, evaluating for the environmental context that the robot is in, the expected progress methods which use the robot’s movement capabilities to evaluate for progress that has been made from a milestone checkpoint, and the fixed radius methods which use user-selected parameters based on mission objectives to evaluate for the progress that has been made from a milestone checkpoint. The proposed control policies are the metric-based sequential policies which use benchmark robot performance metrics to select the order in which the planner combinations are to be launched, the context-based pairs policies which evaluate what happens when switching between only two planner combinations, and the restart policy which simply relaunches a new instance of the same planner combination. The study evaluated which monitoring and control policies, when paired, contributed to improved navigation performance and which policies contributed to degraded navigation performance by evaluating how close the robot was able to get to the final mission goal. Although specific methods were evaluated, the contributions of the project extend beyond the results by offering both a template for metareasoning approaches with regard to navigation as well as replicable algorithms that may be applied to any autonomous ground robot system. Additionally, this thesis presents ideas for additional research in order to determine under which conditions metareasoning will improve navigation.