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arXiv · 2605.01431

Point-to-Cloud NMPC with Smooth Avoidance Constraints

Brener G. Ferreira·Vinicius M. Gonçalves·Marcelo A. Santos·Guilherme V. Raffo
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Abstract

This paper proposes a finite-horizon optimal control strategy for set-point tracking using a nonlinear model predictive control framework with integrated avoidance capabilities. The formulation employs a smooth point-to-cloud distance metric that ensures continuously differentiable and numerically well-conditioned gradients, even in the presence of regions with complex and nonconvex geometries. This smoothness allows safety constraints to be formulated consistently and differentiably through control barrier functions, resulting in a reliable avoidance behavior for the closed-loop system. Additionally, stationary artificial variables are introduced in the optimal control problem to preserve feasibility under changing set-points. The proposed approach is validated through numerical experiments of an aerial robot, demonstrating accurate tracking and smooth obstacle avoidance in complex environments.

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Brener G. Ferreira, Vinicius M. Gonçalves, Marcelo A. Santos, Guilherme V. Raffo. 2026-05-02. Point-to-Cloud NMPC with Smooth Avoidance Constraints. https://arxiv.org/abs/2605.01431

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