基于约束优化和自适应调度的基于模型扩散的运动规划
Motion Planning with Model-Based Diffusion via Constraint Optimization and Adaptive Scheduling
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中文总结 AI 辅助
针对高度非凸约束环境下单机器人运动规划难题，提出MD-COAS方法，统一软扩散先验与硬投影算子，自适应调度并共同优化安全约束及扩散调度，实验证明其在多方面优于基线规划器。
中文摘要 AI 辅助
在高度非凸约束环境中的单机器人运动规划具有挑战性。近期基于模型的扩散（MBD）方法将其重铸为轨迹优化。现有工作虽有进展但存在局限，如缺乏统一框架整合安全约束方式、忽视扩散调度变化。本文提出基于约束优化和自适应调度的基于模型扩散（MD-COAS），统一了软扩散先验和硬投影算子，自适应调度并共同优化安全约束及扩散调度。实验表明该方法在安全性、成功率、收敛速度和最终成本方面优于基线规划器。
英文摘要
Single-Robot Motion Planning (SRMP) in highly non-convex constrained environments, where robots must satisfy collision-free guarantees, dynamic feasibility, and task-related constraints, is challenging under complex constraints and computational limits. Recent Model-Based Diffusion (MBD) approaches recast the SRMP as trajectory optimization that samples from a posterior over trajectories, using known dynamics, and analytically estimates the score function from rollout samples to guide diffusion denoising toward a low-cost, clean trajectory without demonstration learning. While existing works further adapt MBD to constrained environments and showcase promising performance, they are still limited by (1) enforcing safety either via soft feasibility diffusion priors or hard projection operators, but lack a unified framework to integrate both, and (2) fixing safety enforcement to neglect the changing of diffusion scheduling. Therefore, we introduce Model-Based Diffusion via Constraint Optimization and Adaptive Scheduling (MD-COAS) for SRMP that unifies the inexact Augmented Lagrangian Method (iALM) soft diffusion prior with a Convex Feasible Set (CFS)-based hard projection operator, and adaptively schedules and co-optimizes safety enforcement, along with diffusion scheduling. Experiments demonstrate that our method achieves higher safety \& success rates, faster convergence, and lower final costs than baseline planners on randomly generated highly non-convex 2D benchmarks and a 7-DoF robot arm avoidance task.
