用神经网络动态约束机器人避障，实时且更安全。
Residual Neural Terminal Constraint for MPC-based Collision Avoidance in Dynamic Environments
- 用神经残差修正距离函数，实现实时安全集估计。
- 仿真与实测中成功率最高提升30%，计算量相当。
- 适合需要高实时性与可靠避障的机器人系统。
本文提出一种混合模型预测控制（MPC）局部规划器，利用基于学习的时变安全集近似作为MPC终端约束。该安全集可表示为哈密顿-雅可比（HJ）可达性分析所得值函数的零上水平集，但其无法实时计算。我们利用HJ值函数可分解为符号距离函数（SDF）与非负残差函数之差的性质，将残差建模为输出非负的神经网络，并从计算出的SDF中减去该残差，从而得到一个在设计上至少与SDF一样安全的实时值函数估计。此外，通过超网络参数化神经残差，进一步提升实时性能与泛化能力。所提方法在仿真和硬件实验中与三种先进方法对比，成功率最高提升30%，计算开销相似，且生成轨迹旅行时间低。
原文摘要 · Abstract (English)
In this paper, we propose a hybrid MPC local planner that uses a learning-based approximation of a time-varying safe set, derived from local observations and applied as the MPC terminal constraint. This set can be represented as a zero-superlevel set of the value function computed via Hamilton-Jacobi (HJ) reachability analysis, which is infeasible in real-time. We exploit the property that the HJ value function can be expressed as a difference of the corresponding signed distance function (SDF) and a non-negative residual function. The residual component is modeled as a neural network with non-negative output and subtracted from the computed SDF, resulting in a real-time value function estimate that is at least as safe as the SDF by design. Additionally, we parametrize the neural residual by a hypernetwork to improve real-time performance and generalization properties. The proposed method is compared with three state-of-the-art methods in simulations and hardware experiments, achieving up to 30\% higher success rates compared to the best baseline while requiring a similar computational effort and producing high-quality (low travel-time) solutions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。