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

2025-07-18

AI Summary

Under dynamic oceanic conditions, underwater manipulators face challenges in reusing prior motion experience and adapting to real-time environmental uncertainties. To address these issues, this paper proposes an adaptive heuristic motion planning framework. The framework constructs a heuristic motion space and integrates a Bayesian network for online modeling and dynamic updating of environmental uncertainty. Within this space, an improved probabilistic roadmap (PRM) optimizes a composite cost function while incorporating real-time sensor data for uncertainty-aware inference. Simulation and physical underwater experiments demonstrate that the proposed method significantly outperforms existing approaches in path success rate, energy consumption, and execution time, achieving superior computational efficiency, robustness, and environmental adaptability. The core innovation lies in the synergistic mechanism between heuristic motion memory and context-aware Bayesian inference.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Heuristic Search
• Planning, Routing, and Scheduling: Planning under Uncertainty

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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. By leveraging HMS, our framework significantly reduces the search space, thereby boosting computational performance and enabling real-time planning capabilities. Bayesian Networks are utilized to dynamically update uncertainty estimates based on real-time sensor data and environmental conditions, thereby refining the joint probability of path success. Through extensive simulations and real-world test scenarios, we showcase the advantages of our method in terms of enhanced performance and robustness. This probabilistic approach significantly advances the capability of autonomous underwater robots, ensuring optimized motion planning in the face of dynamic marine challenges.

Problem

• Enhance motion planning for underwater manipulation in dynamic environments
• Utilize prior motion experiences and adapt to real-time uncertainties
• Optimize paths considering distance, uncertainty, energy, and execution time

Innovation

• Adaptive Heuristic Motion Planner with HMS
• PRM algorithm optimizes composite cost function
• Bayesian Networks update real-time uncertainty estimates