A Reliable Robot Motion Planner in Complex Real-world Environments via Action Imagination

Humans and animals can make real-time adjustments to movements by imagining their action outcomes to prevent unanticipated or even catastrophic motion failures in unknown unstructured environments. Action imagination, as a refined sensorimotor strategy, leverages perception-action loops to handle physical interaction-induced uncertainties in perception and system modeling within complex systems. Inspired by the action-awareness capability of animal intelligence, this study proposes an imagination-inspired motion planner (I-MP) framework that specifically enhances robots' action reliability by imagining plausible spatial states for approaching. After topologizing the workspace, I-MP build perception-action loop enabling robots autonomously build contact models. Leveraging fixed-point theory and Hausdorff distance, the planner computes convergent spatial states under interaction characteristics and mission constraints. By homogenously representing multi-dimensional environmental characteristics through work, the robot can approach the imagined spatial states via real-time computation of energy gradients. Consequently, experimental results demonstrate the practicality and robustness of I-MP in complex cluttered environments.