Motion planning for lunar rover based on behavior decision field Q-learning

Autonomous behavior motion planning with kinematics constrains is improved for lunar rovers. The behavior controller is implemented by fuzzy system, and the hybrid architecture is adopted to ensure both the robustness and optimization. Velocity and turning radius are chosen as the output of fuzzy behavior to decouple the velocity and angle velocity. Behavior decision field (BDF) is presented, and kinematics constrains are re-written as the reward functions within the Q-learning process. This bottom-to-top design ensures motion planning the kinematics quality in BDF. Experiment results are shown to demonstrate the effectiveness and traceability of the trajectory.