Point-to-Cloud NMPC with Smooth Avoidance Constraints
For robotic systems requiring safe navigation in complex environments, this work provides a numerically stable and differentiable formulation for obstacle avoidance in NMPC.
This paper introduces a nonlinear model predictive control strategy for set-point tracking with smooth avoidance constraints, validated on an aerial robot for accurate tracking and smooth obstacle avoidance in complex environments.
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.