用神经网络提升移动机械臂的避障效率，实现快速安全轨迹规划。
Fast and Safe Trajectory Optimization for Mobile Manipulators With Neural Configuration Space Distance Field
- 提出广义配置空间距离场，将避障计算从复杂映射转为平滑隐式距离。
- 在真实场景中实现毫秒级响应，支持上千个约束并行处理。
- 适合需要快速重规划的移动机器人系统，如仓储物流、救援搜救。
移动机械臂通过协调基座与臂部运动，可实现灵活、长时程操作，但在复杂受限空间中进行全身轨迹优化仍面临高维非凸性及快速准确碰撞推理的挑战。配置空间距离场（CDF）使固定基座机械臂能直接在配置空间中以光滑隐式距离建模碰撞，避免非线性配置到工作空间映射的同时保持精确的全肢体几何，并提供优化友好的碰撞代价。然而，将其扩展至移动机械臂受限于无界工作空间和更紧密的基座-臂部耦合。本文提出广义配置空间距离场（GCDF），将CDF推广至具有平移与旋转关节的移动机器人，在无界空间中保留紧致的基臂耦合特性。证明了GCDF保持欧氏类局部距离结构并精确编码全肢体几何；构建数据生成与训练流程，得到连续神经GCDF，具备准确值与梯度，支持高效GPU批量查询。基于此表示，开发高性能序列凸优化框架，通过（i）在线指定神经约束，（ii）结合稀疏感知的主动集检测与数千约束并行批量评估，（iii）增量约束管理实现场景变化下的快速重规划，显著提升求解效率与实时性。
原文摘要 · Abstract (English)
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.
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