Context-Aware Behavior Learning with Heuristic Motion Memory for Underwater Manipulation

Understand

Autonomous motion planning is critical for efficient and safe underwater manipulation in dynamic marine environments.

• Current motion planning methods often fail to effectively utilize prior motion experiences and adapt to real-time uncertainties inherent in underwater settings.
• In this paper, we introduce an Adaptive Heuristic Motion Planner framework that integrates a Heuristic Motion Space (HMS) with Bayesian Networks to enhance motion planning for autonomous underwater manipulation.
• Our approach employs the Probabilistic Roadmap (PRM) algorithm within HMS to optimize paths by minimizing a composite cost function that accounts for distance, uncertainty, energy consumption, and execution time.