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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.01431 |
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| _version_ | 1866915974256525312 |
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| author | Ferreira, Brener G. Gonçalves, Vinicius M. Santos, Marcelo A. Raffo, Guilherme V. |
| author_facet | Ferreira, Brener G. Gonçalves, Vinicius M. Santos, Marcelo A. Raffo, Guilherme V. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_01431 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Point-to-Cloud NMPC with Smooth Avoidance Constraints Ferreira, Brener G. Gonçalves, Vinicius M. Santos, Marcelo A. Raffo, Guilherme V. Systems and Control 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. |
| title | Point-to-Cloud NMPC with Smooth Avoidance Constraints |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2605.01431 |