動的環境におけるMPCベース衝突回避のための残差ニューラル終端制約
Residual Neural Terminal Constraint for MPC-based Collision Avoidance in Dynamic Environments
MPCの終端制約として、局所観測から得られる時変安全集合を学習で近似するハイブリッドMPCを提案。HJ値関数をSDFと非負残差の差で表現し、ニューラルネットで残差をモデル化することでリアルタイム性と安全性を両立した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
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著者: Bojan Derajić, Mohamed-Khalil Bouzidi, Sebastian Bernhard, Wolfgang Hönig
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
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.
