Goal assignment and trajectory planning for large teams of interchangeable robots

Turpin, Matthew; Mohta, Kartik; Michael, Nathan; Kumar, Vijay

doi:10.1007/s10514-014-9412-1

Cited by 89 publications

(58 citation statements)

References 23 publications

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“…Online sequential convex programming has been employed by Augugliaro et al (2012) and Chen et al (2015) to compute collision-free trajectories for multiple Micro Air Vehicles (MAVs), but without considering formations. The assignment of robots to the target positions in the formation is another optimization problem that was solved with a centralized algorithm by Turpin et al (2014) or with a distributed algorithm, albeit in environments without obstacles, by Montijano and Mosteo (2014) and Morgan et al (2016). Building upon the centralized, yet online, method by Alonso-Mora et al (2017), we propose an optimization and consensus based approach to reconfigure the formation in dynamic environments, which is distributed and online.…”

Section: Related Workmentioning

confidence: 99%

Distributed multi-robot formation control in dynamic environments

et al. 2018

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This paper presents a distributed method for formation control of a homogeneous team of aerial or ground mobile robots navigating in environments with static and dynamic obstacles. Each robot in the team has a finite communication and visibility radius and shares information with its neighbors to coordinate. Our approach leverages both constrained optimization and multi-robot consensus to compute the parameters of the multi-robot formation. This ensures that the robots make progress and avoid collisions with static and moving obstacles. In particular, via distributed consensus, the robots compute (a) the convex hull of the robot positions, (b) the desired direction of movement and (c) a large convex region embedded in the four dimensional position-time free space. The robots then compute, via sequential convex programming, the locally optimal parameters for the formation to remain within the convex neighborhood of the robots. The method allows for reconfiguration. Each robot then navigates towards its assigned position in the target collision-free formation via an individual controller that accounts for its dynamics. This approach is efficient and scalable with the number of robots. We present an extensive evaluation of the communication requirements and verify the method in simulations with up to sixteen quadrotors. Lastly, we present experiments with four real quadrotors flying in formation in an environment with one moving human.

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Section: Related Workmentioning

confidence: 99%

Distributed multi-robot formation control in dynamic environments

et al. 2018

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“…Here, the task-allocation and trajectory-generation problems must be simultaneously solved. Paradoxically, the ability to assign goals reduces the problem's computational complexity, and a cleverly chosen cost function for goal assignment can directly guarantee trajectory safety (119,120). In labeled problems, by contrast, robots must visit predefined, noninterchangeable goal positions.…”

Section: Multirobot Systemsmentioning

confidence: 99%

Autonomous Flight

Tang

Kumar

2018

Annu. Rev. Control Robot. Auton. Syst.

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This review surveys the current state of the art in the development of unmanned aerial vehicles, focusing on algorithms for quadrotors. Tremendous progress has been made across both industry and academia, and full vehicle autonomy is now well within reach. We begin by presenting recent successes in control, estimation, and trajectory planning that have enabled agile, highspeed flight using low-cost onboard sensors. We then examine new research trends in learning and multirobot systems and conclude with a discussion of open challenges and directions for future research. 6.1

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“…Their solution, though scalable, does not use motion primitives for planning, thus is not suitable for robots that have complex dynamics. Recently, Turpin et al [25] proposed an algorithm for simultaneous planning and goal assignment for interchangeable robots and demonstrated their algorithm to work on a group of quadrotors. However, our objective is to solve the planning problem where the goals are pre-assigned to the robots.…”

Section: Related Workmentioning

confidence: 99%

Implan: Scalable Incremental Motion Planning for Multi-Robot Systems

Saha

Ramaithitima

Kumar

et al. 2016

2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS)

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Abstract-We consider the collision-free motion planning problem for a group of robots using a library of motion primitives. To cope with the complexity of the problem, we introduce an incremental algorithm based on an SMT solver, where we divide the robots into small groups based on a priority assignment algorithm. The priority assignment algorithm assigns priorities to the robots in such a way that the robots do not block the cost-optimal trajectories of the other robots. While the priority assignment algorithm attempts to assign distinct priorities to the robots, the algorithm ends up with assigning the same priority to some robots due to the dependencies among themselves. The algorithm includes the robots with the same priority in the same group. Our incremental algorithm then considers the robot groups one by one based on their priority and synthesizes the trajectories for the group of robots together. While synthesizing the trajectories for the robots in one group, the algorithm considers the higher priority robots as dynamic obstacles, and introduces a minimal delay in executing the cost-optimal trajectories to avoid collision with the higher priority robots. We apply our method to synthesize trajectories for a group of quadrotors in our lab space. Experimental results show that we can synthesize trajectories for tens of robots with complex dynamics in a reasonable time.

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Goal assignment and trajectory planning for large teams of interchangeable robots

Cited by 89 publications

References 23 publications

Distributed multi-robot formation control in dynamic environments

Distributed multi-robot formation control in dynamic environments

Autonomous Flight

Implan: Scalable Incremental Motion Planning for Multi-Robot Systems

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