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 o r even catastrophic motion failures in unknown unstructured environments. Action imagination, as a refined sensorimotor strategy, leverages perception - action loops to handle physical interaction -induced uncer tainties in perception and system modeling within complex systems. Inspired by the action -awareness capability of animal intelligence, this study proposes a n imagination - inspired motion planner (I -MP) framework that speci fically 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 compute s 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 o f energy gradients. Consequently, e xperimental results demonstrate the practicality and robustness of IMP in complex cluttered environments.