This paper studies decentralized safe path following for multiple quadrotors on intersecting paths, where safety requires both collision avoidance and strict adherence to pre-assigned routes. The proposed controller reformulates transverse feedback linearization as a constrained quadratic program with four equality constraints: two enforce convergence to and strict adherence to the path, and two prescribe the desired speed and heading. Safety is achieved by relaxing only the along-path speed constraint, while the path and heading constraints remain hard. Under the stated assumptions, and for admissible initial conditions, the controller guarantees that all agents converge to and thereafter follow their assigned paths, avoid collisions, and avoid attitude singularities. We evaluate the controller in the Drake physics engine on non-planar intersecting paths and compare it against two nominal-plus-safety-filter cascades. Code and additional results are available at https://gradslab.github.io/safe_multiquad_pf/.
Figures & tables
Fig. 1: Manifold interpretation of the proposed relaxation. The nominal TFL controller stabilizes the intersection of the path-following and secondary-objective manifolds. The scalar slack δ relaxes the along-path speed while the transverse and heading equalities remain hard, so the path-following manifold remains locally exponentially attractive along admissible closed-loop trajectories.
Fig. 2: Path following in Drake for four quadrotors on intersecting circular paths: proposed (solid), TFL [ 17 ] + filter (dashed), SE(3) [ 27 ] + filter (dotted). Left: three-dimensional trajectories. Right: top view. From off-path initial conditions, the proposed controller converges to and, after 15 s, remains on its assigned path to within 0.5 mm, while both cascades deviate from theirs to avoid collisions.
Fig. 3: Output channels and minimum pairwise distance for the circle scenario: proposed (solid), TFL [ 17 ] + filter (dashed), SE(3) [ 27 ] + filter (dotted). The proposed controller drives the transverse and altitude channels to their references and resolves conflicts through the along-path speed, while both cascades perturb every channel.
We study high-order safety-critical control of quadrotor teams under bounded inputs and pairwise collision-avoidance constraints. Squared-distance barriers may lose thrust effectiveness when the relative displacement is orthogonal to the available thrust directions, while nonsingular constraints may still be jointly infeasible under shared bounds. We characterize both phenomena through pairwise effectiveness and aggregate feasibility measures. A torque-aware dynamic extension exposes attitude torques in a fourth-order barrier and prevents the extended-input row from vanishing under positive thrust. Gaussian processes directly learn the fourth-order HOCBF residual, providing robust margins without differentiating unknown perturbations. Under residual-bound and persistent-feasibility assumptions, the resulting QP guarantees collision avoidance and recovers the nominal input whenever it satisfies the robust safety and actuator constraints.
Omayra Yago Nieto, Leonardo Colombo
Universidad Polit´ecnica de Madrid, Spain · Centre for Automation and Robotics (CSIC-UPM), Ctra. M300 Campo Real, Km 0,200, Arganda del Rey - 28500 Madrid, Spain
Multi-quadrotor passage planning requires coordinated spatial routes and passage times under vehicle dynamics and actuation constraints. Optimizing these decisions jointly couples temporal allocation, nonlinear motion, and inter-vehicle interactions, creating a computational challenge for local trajectory generation. We propose a learning-optimization framework that predicts spatial waypoints and ordered passage events for independently solved quadrotor planners. A common physical time grid aligns the vehicles, with intermediate events fixed by the networks and terminal progress optimized locally to permit different completion metrics. Differentiable sequential convex programming incorporates rigid-body dynamics and rotor-thrust bounds, allowing collision and completion gradients to train both networks through the generated trajectories. In a two-quadrotor evaluation across 24 layouts, the adapted policy passes physical feasibility and sampled separation checks in all five held-out layouts. Their mean completion metric decreases by 1.52% relative to the inherited policy. The median forward time is 21.01 ms on the held-out exchange layout with matching numerical trajectory references. These results demonstrate that learned spatial and event parameters can coordinate independent rigid-body planners while preserving local vehicle constraints in the evaluated scenarios.
Yunhan Shi, Jia Deng, Zehao Jia +2
School of Astronautics, Beihang University, Beijing 100191, China · Astroup(Beijing) Technology Co., Ltd.
Safety-critical quadrotor navigation in cluttered environments typically relies on hierarchical architectures in which a high-level safety filter modifies position references that are then tracked by a separate attitude controller. However, this separation can create a mismatch between translational safety objectives and attitude evolution. To address this gap, we propose a safety controller based on fourth-order control barrier functions (CBFs) that explicitly incorporates the full nonlinear quadrotor dynamics, including attitude dynamics and torque-level actuation. Furthermore, we apply the integral high-order CBF technique to resolve the mixed-relative degree issue in the derived constraint formulation. The resulting formulation enforces obstacle-avoidance constraints while optimizing control torques, granting the safety layer direct authority over attitude behavior and eliminating the hierarchical disconnect inherent in reference-tracking safety filters. We validate the approaches in both high-fidelity simulation and real-world flight experiments, demonstrating reliable constraint satisfaction and successful obstacle avoidance. These results suggest that torque-level high-order CBFs provide a practical pathway to provably safer quadrotor navigation under full rigid-body dynamics.