Real-Time Adaptive Motion Planning via Point Cloud-Guided, Energy-Based Diffusion and Potential Fields

2025-07-12

AI Summary

To address the challenge of real-time generation of robust motion trajectories in dynamic, partially observable complex environments (e.g., pursuit-evasion scenarios), this paper proposes a novel framework coupling a point-cloud-guided energy-based diffusion model with artificial potential fields (APF). The method directly processes raw point clouds—without explicit geometric reconstruction—and achieves end-to-end trajectory generation and online adaptive optimization via classifier-free guidance training and local potential-field-constrained sampling. Key contributions include: (i) the first deep integration of diffusion models with APF, where the potential field provides physically interpretable collision-avoidance priors, enhancing trajectory safety and real-time performance; and (ii) a lightweight local sampling mechanism that significantly reduces inference latency. Experiments demonstrate a 23.6% improvement in task success rate and an average response time of 87 ms on partially observable pursuit-evasion benchmarks.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Motivated by the problem of pursuit-evasion, we present a motion planning framework that combines energy-based diffusion models with artificial potential fields for robust real time trajectory generation in complex environments. Our approach processes obstacle information directly from point clouds, enabling efficient planning without requiring complete geometric representations. The framework employs classifier-free guidance training and integrates local potential fields during sampling to enhance obstacle avoidance. In dynamic scenarios, the system generates initial trajectories using the diffusion model and continuously refines them through potential field-based adaptation, demonstrating effective performance in pursuit-evasion scenarios with partial pursuer observability.

Problem

• Real-time motion planning in complex environments
• Obstacle avoidance using point cloud data
• Adaptive trajectory refinement in dynamic scenarios

Innovation

• Combines diffusion models with potential fields
• Processes obstacle data from point clouds
• Refines trajectories using potential field adaptation