Robust collision avoidance for multiple micro aerial vehicles using nonlinear model predictive control

Kamel, Mina; Alonso–Mora, Javier; Siegwart, Roland; Nieto, Juan

doi:10.1109/iros.2017.8202163

Cited by 111 publications

(114 citation statements)

References 24 publications

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“…This results in a comparatively low-dimension nonlinear program (NLP), which can be solved efficiently by gradient-based solvers [12]. Even though this solution cannot guarantee global optimality, its reduced computational cost makes this strategy to be widely adopted in most time-critical motion planning tasks, such as model predictive control for aerial robots [5], [6], [15].…”

Section: Related Workmentioning

confidence: 99%

“…As presented in Table I, our approach solves a conservative approximation of [4] over 142 times faster at the price 4% of optimality. Our computation time falls in the range of [5], [6], which have been widely used for fast real-time motion planning. The one-horizon benchmark has been executed from the optimization framework CasADi [22], being publicly available on-line to be reproduced 2 .…”

Section: One-horizon Benchmarkmentioning

confidence: 99%

“…Strong assumptions need to be made to find tractable solutions for fast real-time applications. This leads to over-conservative obstacle models that ensure collision-free operation but drastically reduce the remaining free space [5], [6], compromising the problem's feasiblity when multiple obstacles arise. As a result, reducing conservatism in motion planning algorithms while providing safety guarantees has become a major problem and the subject of active research [4], [6]- [11].…”

Section: Introductionmentioning

confidence: 99%

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A Real-Time Approach for Chance-Constrained Motion Planning With Dynamic Obstacles

Castillo-Lopez

Ludivig

Sajadi-Alamdari

et al. 2020

IEEE Robot. Autom. Lett.

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Uncertain dynamic obstacles, such as pedestrians or vehicles, pose a major challenge for optimal robot navigation with safety guarantees. Previous work on motion planning has followed two main strategies to provide a safe bound on an obstacle's space: a polyhedron, such as a cuboid, or a nonlinear differentiable surface, such as an ellipsoid. The former approach relies on disjunctive programming, which has a relatively high computational cost that grows exponentially with the number of obstacles. The latter approach needs to be linearized locally to find a tractable evaluation of the chance constraints, which dramatically reduces the remaining free space and leads to over-conservative trajectories or even unfeasibility. In this work, we present a hybrid approach that eludes the pitfalls of both strategies while maintaining the original safety guarantees. The key idea consists in obtaining a safe differentiable approximation for the disjunctive chance constraints bounding the obstacles. The resulting nonlinear optimization problem is free of chance constraint linearization and disjunctive programming, and therefore, it can be efficiently solved to meet fast real-time requirements with multiple obstacles. We validate our approach through mathematical proof, simulation and real experiments with an aerial robot using nonlinear model predictive control to avoid pedestrians.

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Section: Related Workmentioning

confidence: 99%

Section: One-horizon Benchmarkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Real-Time Approach for Chance-Constrained Motion Planning With Dynamic Obstacles

Castillo-Lopez

Ludivig

Sajadi-Alamdari

et al. 2020

IEEE Robot. Autom. Lett.

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“…2 shows a scenario with two agents and the increase in the size of an agent's bounding ellipsoid as perceived by another agent. This is a simple approach to account for uncertainty, which could be extended to more sophisticated methods, as shown in [37] or [22].…”

Section: Modeling Uncertaintymentioning

confidence: 99%

DCAD: Decentralized Collision Avoidance With Dynamics Constraints for Agile Quadrotor Swarms

Arul

Manocha

2020

IEEE Robot. Autom. Lett.

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We present a novel, decentralized collision avoidance algorithm for navigating a swarm of quadrotors in dense environments populated with static and dynamic obstacles. Our algorithm relies on the concept of Optimal Reciprocal Collision Avoidance (ORCA) and utilizes a flatness-based Model Predictive Control (MPC) to generate local collision-free trajectories for each quadrotor. We feedforward linearize the non-linear dynamics of the quadrotor and subsequently use this linearized model in our MPC framework. Our method approach tends to compute safe trajectories that avoid quadrotors from entering each other's downwash regions during close proximity maneuvers. In addition, we account for the uncertainty in the position and velocity sensor data using Kalman filter. We evaluate the performance of our algorithm with other state-of-the-art decentralized methods and demonstrate its superior performance in terms of smoothness of generated trajectories and lower probability of collision during high velocity maneuvers.

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“…A vision-based UAV needs three main components to navigate effectively in an environment populated of possibly dynamic obstacles: 1) A reactive control strategy, to accurately track a desired trajectory while reducing the motors effort; 2) A reliable collision avoidance module, to safely navigate the environment even in presence of dynamic, unmodeled obstacles; 3) An adaptive, perception aware, on-line planner, to support the vision-based state estimation or to constantly keep the line-of-sight with a possible reference target. A wide literature is available addressing individually or in pairs these tasks (among others, [21], [11], [25]). However, they have rarely been addressed together, particularly when dealing with unexpected and moving obstacles.…”

Section: A Related Workmentioning

confidence: 99%

Joint Vision-Based Navigation, Control and Obstacle Avoidance for UAVs in Dynamic Environments

Potena

Nardi

Pretto

2019

2019 European Conference on Mobile Robots (ECMR)

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This work addresses the problem of coupling vision-based navigation systems for Unmanned Aerial Vehicles (UAVs) with robust obstacle avoidance capabilities. The former is formulated by a maximization of the point of interest visibility, while the latter is modeled by ellipsoidal repulsive areas. The whole problem is transcribed into an Optimal Control Problem (OCP), and solved in a few milliseconds by leveraging state-ofthe-art numerical optimization. The resulting trajectories are then well suited to achieve the specified goal location while avoiding obstacles by a safety margin and minimizing the probability to loose track with the target of interest. Combining this technique with a proper ellipsoid shaping (e.g. augmenting the shape with the obstacle velocity, or the obstacle detection uncertainties) results in a robust obstacle avoidance behaviour.We validate our approach within extensive simulated experiments demonstrating (i) capability to satisfy all the constraints, and (ii) the avoidance reactivity even in challeging situations. We release with this paper the open source implementation.

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Robust collision avoidance for multiple micro aerial vehicles using nonlinear model predictive control

Cited by 111 publications

References 24 publications

A Real-Time Approach for Chance-Constrained Motion Planning With Dynamic Obstacles

A Real-Time Approach for Chance-Constrained Motion Planning With Dynamic Obstacles

DCAD: Decentralized Collision Avoidance With Dynamics Constraints for Agile Quadrotor Swarms

Joint Vision-Based Navigation, Control and Obstacle Avoidance for UAVs in Dynamic Environments

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