Decentralized prioritized motion planning for multiple autonomous UAVs in 3D polygonal obstacle environments

Ma, Xiaobai; Jiao, Ziyuan; Wang, Zhenkai; Panagou, Dimitra

doi:10.1109/icuas.2016.7502596

Cited by 30 publications

(13 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…But, while FMT methods have better performance in higher dimensional systems, they still require the paths to be centrally calculated. A* can also be used in dense environments for decentralized multi-agent planning when combined with barrier functions [MJWP16], but require the true position of all other agents. While all of these methods require global knowledge and large searches over a discrete set, they can be used as waypoint generators that feed into our method-for use in, e.g., cluttered environments.…”

Section: Related Workmentioning

confidence: 99%

Reciprocal Multi-Robot Collision Avoidance with Asymmetric State Uncertainty

Shah,

Angeris,

Schwager

2021

Preprint

View full text Add to dashboard Cite

We present a general decentralized formulation for a large class of collision avoidance methods and show that all collision avoidance methods of this form are guaranteed to be collision free. This class includes several existing algorithms in the literature as special cases. We then present a particular instance of this collision avoidance method, CARP (Collision Avoidance by Reciprocal Projections), that is effective even when the estimates of other agents' positions and velocities are noisy. The method's main computational step involves the solution of a small convex optimization problem, which can be quickly solved in practice, even on embedded platforms, making it practical to use on computationally-constrained robots such as quadrotors. This method can be extended to find smooth polynomial trajectories for higher dynamic systems such at quadrotors. We demonstrate this algorithm's performance in simulations and on a team of physical quadrotors. Our method finds optimal projections in a median time of 17.12ms for 285 instances of 100 randomly generated obstacles, and produces safe polynomial trajectories at over 60hz on-board quadrotors. Our paper is accompanied by an open source Julia implementation and ROS package.

show abstract

Section: Related Workmentioning

confidence: 99%

Reciprocal Multi-Robot Collision Avoidance with Asymmetric State Uncertainty

Shah,

Angeris,

Schwager

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…7. Allocation task, decentralized framework, and known environment Ma XB et al (2016 studied decentralized motion planning for a UAV group moving in a 3D urban-like environment with polygonal obstacles. A prioritized A * algorithm was used to generate the shortest paths for UAVs to reach their targets, and a coordination method based on barrier functions was used to generate collision-free trajectories.…”

Section: Classification Of Existing Researchmentioning

confidence: 99%

“…Wu JY et al 2017AT-CF-KE 3D, battlefield = -√ ----Ergezer and Leblebicioğlu 2014AT-CF-KE 3D, mountain = ------Çakıcı et al 2016AT-CF-KE 3D, mountain = ------ Sahingoz (2013Sahingoz ( , 2014 AT-CF-KE 2D, obstacle-free = ------Cekmez et al 2016AT-CF-KE 2D, battlefield = ------Li XH et al 2016AT-CF-KE 2D, farmland = ------Li T et al 2016AT-CF-KE 2D, obstacle-free = ------Manyam et al 2017AT-CF-KE 2D, obstacle-free = ------Sørli et al 2017AT-CF-KE 2D, obstacle-free = ------Harounabadi et al 2018AT-CF-KE 2D, obstacle-free = ------Binol et al 2018AT-CF-KE 2D, road network = ------Ning et al 2019AT-CF-KE 2D, battlefield = ------Cho et al 2019AT-CF-KE 2D, obstacle-free ------Zhao M et al 2017AT-CF-KE 3D, battlefield = ----√ -Zhang X et al 2014AT-CF-KE 2D, obstacle-free = ------Qin et al 2018AT-CF-KE 2D, obstacle-free = ------Yang J et al 2018AT-CF-KE 2D, obstacle-free = ------Quintin et al 2017AT-CF-KE 2D, obstacle ------Zhao Z et al 2019AT-CF-KE 3D, obstacle = √ -----Su et al 2016AT-CF-UE 3D, battlefield = √ √ ---- Ma XB et al (2016 AT-DF-KE 3D, urban-like = -√ ----Causa et al 2018AT 2019AT-DF-UE 2D, obstacle √ √ ---- Yang et al (2019aYang et al ( , 2019b AT-HF-KE 2D, obstacle √ ---√ -AT: allocation task; CF: centralized framework; DF: decentralized framework; HF: hybrid framework; KE: known environment; UE: unknown environment. 1: real time; 2: collision avoidance among UAVs; 3: connectivity; 4: formation; 5: synchronicity; 6: heading coordination.…”

Section: Classification Of Existing Researchmentioning

confidence: 99%

A review of cooperative path planning of an unmanned aerial vehicle group

Zhang

Xin

Dou

et al. 2020

Front Inform Technol Electron Eng

View full text Add to dashboard Cite

As a cutting-edge branch of unmanned aerial vehicle (UAV) technology, the cooperation of a group of UAVs has attracted increasing attention from both civil and military sectors, due to its remarkable merits in functionality and flexibility for accomplishing complex extensive tasks, e.g., search and rescue, fire-fighting, reconnaissance, and surveillance. Cooperative path planning (CPP) is a key problem for a UAV group in executing tasks collectively. In this paper, an attempt is made to perform a comprehensive review of the research on CPP for UAV groups. First, a generalized optimization framework of CPP problems is proposed from the viewpoint of three key elements, i.e., task, UAV group, and environment, as a basis for a comprehensive classification of different types of CPP problems. By following the proposed framework, a taxonomy for the classification of existing CPP problems is proposed to describe different kinds of CPPs in a unified way. Then, a review and a statistical analysis are presented based on the taxonomy, emphasizing the coordinative elements in the existing CPP research. In addition, a collection of challenging CPP problems are provided to highlight future research directions.

show abstract

“…Decoupled methods, on the other hand, are able to be fast enough for real-time application. For example, the method used in [8] is a decoupled multi-agent motion planning method. It uses an algorithm for finding the path for the respective AGVs, but this method does not deal with conflict scenarios.…”

Section: Introductionmentioning

confidence: 99%

Multi-AGV Path Planning for Indoor Factory by Using Prioritized Planning and Improved Ant Algorithm

Zhang

Wang

et al. 2018

J. Eng. Technol. Sci.

View full text Add to dashboard Cite

Multiple automated guided vehicle (multi-AGV) path planning in manufacturing workshops has always been technically difficult for industrial applications. This paper presents a multi-AGV path planning method based on prioritized planning and improved ant colony algorithms. Firstly, in dealing with the problem of path coordination between AGVs, an improved priority algorithm is introduced, where priority is assigned based on the remaining battery charge of the AGVs, which improves the power usage efficiency of the AGVs. Secondly, an improved ant colony algorithm (IAC) is proposed to calculate the optimal path for the AGVs. In the algorithm, a random amount of pheromone is distributed in the map and the amount of pheromone is updated according to a fitness value. As a result, the computational efficiency of the ant colony algorithm is improved. Moreover, a mutation operation is introduced to mutate the amount of pheromone in randomly selected locations of the map, by which the problem of local optimum is well overcome. Simulation results and a comparative analysis showed the validity of the proposed method.

show abstract

Decentralized prioritized motion planning for multiple autonomous UAVs in 3D polygonal obstacle environments

Cited by 30 publications

References 14 publications

Reciprocal Multi-Robot Collision Avoidance with Asymmetric State Uncertainty

Reciprocal Multi-Robot Collision Avoidance with Asymmetric State Uncertainty

A review of cooperative path planning of an unmanned aerial vehicle group

Multi-AGV Path Planning for Indoor Factory by Using Prioritized Planning and Improved Ant Algorithm

Contact Info

Product

Resources

About