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

robotics › navigation

📄 Abstract

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.

Authors (6)

Wondmgezahu Teshome

Kian Behzad

Octavia Camps

Michael Everett

Milad Siami

Mario Sznaier

Submitted

July 12, 2025

arXiv Category

cs.RO

IEEE Robotics and Automation Letters 10 (2025) 9160-9167

arXiv PDF

Key Contributions

This paper presents a real-time motion planning framework combining energy-based diffusion models with artificial potential fields. It processes point clouds for obstacle information and uses classifier-free guidance and potential fields for adaptive trajectory generation, demonstrating effective performance in pursuit-evasion with partial observability.

Business Value

Enables more robust and adaptive navigation for robots and autonomous systems in dynamic and uncertain environments, crucial for applications like autonomous driving, drone delivery, and search and rescue.

Paper Metadata

Innovation Type

Hybrid Motion Planning Framework

Deployment Feasibility

Moderate to High, requires real-time processing of sensor data (point clouds) and integration with control systems.

Limitations Addressed

The challenge of real-time motion planning in complex environments, especially with partial observability and dynamic obstacles, without requiring complete geometric representations.

Performance Gains

Effective performance in pursuit-evasion scenarios with partial pursuer observability.

Technical Tags

motion planningreal-time adaptationpoint cloudsenergy-based diffusion modelsartificial potential fieldspursuit-evasionclassifier-free guidanceobstacle avoidancepartial observabilitytrajectory generation

Research Topics

Motion PlanningRoboticsReinforcement LearningGenerative ModelsReal-time Control

Methods & Architectures

Energy-based diffusion modelsArtificial potential fieldsClassifier-free guidancePoint cloud processing Diffusion Models

Applications & Tasks

Robotics Autonomous systems Drones Autonomous vehicles Real-time trajectory generationMotion planning in complex environmentsPursuit-evasion scenariosObstacle avoidance Motion planningTrajectory generationNavigation

Datasets & Benchmarks

Benchmarks

Pursuit-evasion scenarios

Related Fields

RoboticsComputer VisionMachine LearningControl TheoryGenerative Models

Keywords

motion planningroboticsdiffusion modelspotential fieldsreal-timepoint cloudsautonomous systemsnavigationpursuit-evasionobstacle avoidance

Academic Context

#Motion Planning#Robotics#Reinforcement Learning#Generative Models#Real-time Control

Commercial Potential

Potential Products

Motion planning software for autonomous vehiclesNavigation systems for drones and robotsReal-time control solutions for dynamic environments

Target Industries

AutomotiveAerospaceLogisticsSecurity / DefenseRobotics

Use Case Examples

Autonomous vehicles navigating complex urban environments with dynamic obstacles.Drones performing surveillance or delivery in unpredictable conditions.Robots operating in cluttered warehouses or construction sites.

Competitive Edge

Combines the generative capabilities of diffusion models with the reactive nature of potential fields for robust real-time motion planning.

Market Opportunity

Large and growing market for autonomous navigation and robotics solutions.

Revenue Models

Licensing of motion planning softwaredevelopment of integrated robotic systems.

Resource Requirements

Compute Needs

Requires significant real-time compute for point cloud processing and diffusion model inference.

Data Requirements

Point cloud data from sensors (e.g., LiDAR), potentially simulated environments for training diffusion models.

Deployment Constraints

Real-time processing capabilities of onboard hardware.,Accuracy and density of point cloud data.,Integration with robot control systems.

Scalability

Scalability depends on the efficiency of the diffusion model and point cloud processing pipeline.

Production Readiness

Maturity Level

Research Prototype

Time to Market

Medium to Long-term, requires extensive testing and validation in real-world robotic systems.

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