AUTHOR=Takemura Reiya , Ishigami Genya 

TITLE=Computationally efficient and sub-optimal trajectory planning framework based on trajectory-quality growth rate analysis

JOURNAL=Frontiers in Robotics and AI

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2022.994437

DOI=10.3389/frobt.2022.994437

ISSN=2296-9144

ABSTRACT=A planetary exploration rover has been used for scientific missions or as precursors for a future manned mission. Rover’s autonomous system is managed by a space-qualified, radiation- hardened onboard computer; hence, the processing performance for such a computer is strictly limited owing to the limitation of power supply. Generally, a computationally efficient algorithm in the autonomous system is favorable. This paper, therefore, presents a computationally efficient and sub-optimal trajectory planning framework for the rover. The framework exploits an incremental search algorithm, which can generate more optimal solutions as the number of iterations increases. Such incremental search is subject to the trade-off between trajectory optimality and computational burden. Therefore, we introduce Trajectory-Quality Growth Rate (TQGR) to statistically analyze the relationship between trajectory optimality and computational cost. This analysis is conducted in several types of terrain; and, the planning stop criterion is estimated. Further, the relation between terrain features and the stop criterion is modeled offline by a machine learning technique. Then, using the criterion predicted by the model, the proposed framework appropriately interrupts the incremental search in online motion planning, resulting in a sub-optimal trajectory with less computational burden. Trajectory planning simulation in various real terrain data validates that the proposed framework can, on average, reduce a computational cost by 47.6% while maintaining 63.8% of trajectory optimality. Further, the simulation result shows the proposed framework still performs well even though the planning stop criterion is not adequately predicted.