Fast and Safe Trajectory Optimization for Mobile Manipulators With Neural Configuration Space Distance Field

2026-01-26

AI Summary

This work addresses the challenges of whole-body trajectory optimization for mobile manipulators in cluttered, constrained environments, where high-dimensional non-convexity and efficient, accurate collision detection are critical. The authors propose the Generalized Configuration Space Distance Field (GCDF), which extends configuration space distance fields to mobile manipulators with both prismatic and revolute joints, preserving local Euclidean distance structure while enabling precise whole-body geometric modeling in unbounded workspaces. Leveraging a neural GCDF representation, they develop a sequential convex optimization framework that integrates GPU-accelerated batch queries, sparse-aware parallel active set detection, and incremental constraint management to efficiently handle thousands of implicit collision constraints. This approach significantly improves the speed and safety of trajectory optimization and replanning in complex environments.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Motion and Path Planning
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Mobile manipulators promise agile, long-horizon behavior by coordinating base and arm motion, yet whole-body trajectory optimization in cluttered, confined spaces remains difficult due to high-dimensional nonconvexity and the need for fast, accurate collision reasoning. Configuration Space Distance Fields (CDF) enable fixed-base manipulators to model collisions directly in configuration space via smooth, implicit distances. This representation holds strong potential to bypass the nonlinear configuration-to-workspace mapping while preserving accurate whole-body geometry and providing optimization-friendly collision costs. Yet, extending this capability to mobile manipulators is hindered by unbounded workspaces and tighter base-arm coupling. We lift this promise to mobile manipulation with Generalized Configuration Space Distance Fields (GCDF), extending CDF to robots with both translational and rotational joints in unbounded workspaces with tighter base-arm coupling. We prove that GCDF preserves Euclidean-like local distance structure and accurately encodes whole-body geometry in configuration space, and develop a data generation and training pipeline that yields continuous neural GCDFs with accurate values and gradients, supporting efficient GPU-batched queries. Building on this representation, we develop a high-performance sequential convex optimization framework centered on GCDF-based collision reasoning. The solver scales to large numbers of implicit constraints through (i) online specification of neural constraints, (ii) sparsity-aware active-set detection with parallel batched evaluation across thousands of constraints, and (iii) incremental constraint management for rapid replanning under scene changes.

Problem

• mobile manipulators
• trajectory optimization
• collision reasoning
• configuration space
• nonconvexity

Innovation

• Generalized Configuration Space Distance Field
• mobile manipulator
• neural implicit representation
• collision-aware trajectory optimization
• sequential convex programming