用深度学习自动调参，让机器人在复杂环境实时避障
A Learning-Based Framework for Collision-Free Motion Planning
- 用神经网络从单张深度图推断最优避障参数
- 实测在仿真和真实机械臂上均实现无碰撞实时规划
- 免人工调参，比传统方法泛化能力更强
本文提出一种基于学习的改进方案，扩展了基于圆形场（CF）的运动规划器，以在复杂环境中高效生成无碰撞轨迹。该方法通过深度神经网络从场景的单张深度图像中推断最优规划增益，克服了人工调参的局限性。系统集成CUDA加速的感知模块、基于预测代理的规划策略，并利用贝叶斯优化在仿真中生成训练数据。所提框架无需手动调参即可实现实时规划，在仿真与实际Franka Emika Panda机械臂上均验证成功。实验表明，该方法在任务完成率和泛化性能上优于经典规划器。
原文摘要 · Abstract (English)
This paper presents a learning-based extension to a Circular Field (CF)-based motion planner for efficient, collision-free trajectory generation in cluttered environments. The proposed approach overcomes the limitations of hand-tuned force field parameters by employing a deep neural network trained to infer optimal planner gains from a single depth image of the scene. The pipeline incorporates a CUDA-accelerated perception module, a predictive agent-based planning strategy, and a dataset generated through Bayesian optimization in simulation. The resulting framework enables real-time planning without manual parameter tuning and is validated both in simulation and on a Franka Emika Panda robot. Experimental results demonstrate successful task completion and improved generalization compared to classical planners.
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