Residual Neural Terminal Constraint for MPC-based Collision Avoidance in Dynamic Environments

• URL: http://arxiv.org/abs/2508.03428v2
• Date: Thu, 28 Aug 2025 08:24:11 GMT
• Title: Residual Neural Terminal Constraint for MPC-based Collision Avoidance in Dynamic Environments
• Authors: Bojan Derajić, Mohamed-Khalil Bouzidi, Sebastian Bernhard, Wolfgang Hönig,
• Abstract summary: We propose a hybrid MPC local planner that uses a learning-based approximation of a time-varying safe set.<n>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.<n>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.
• Score: 3.301014121088537
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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