Optimal Multivehicle Motion Planning Using Bernstein Approximants

Venanzio Cichella; Isaac Kaminer; Claire Walton; Naira Hovakimyan; Antonio M Pascoal

doi:10.1109/TAC.2020.2999329

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Optimal Multivehicle Motion Planning Using Bernstein Approximants

Journal article

Peer reviewed

Optimal Multivehicle Motion Planning Using Bernstein Approximants

Venanzio Cichella, Isaac Kaminer, Claire Walton, Naira Hovakimyan and Antonio M Pascoal

IEEE transactions on automatic control, Vol.66(4), pp.1453-1467

04/2021

DOI: 10.1109/TAC.2020.2999329

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Abstract

This article presents a computational framework to efficiently generate feasible and safe trajectories for multiple autonomous vehicle operations. We formulate the optimal motion planning problem as a continuous-time optimal control problem, and approximate its solutions in a discretized setting using Bernstein polynomials. The latter possess convenient properties that allow to efficiently compute and enforce constraints along the vehicles' trajectories, such as maximum speed and angular rates, minimum distance between trajectories and between the vehicles and known obstacles, etc. Thus, the proposed method is particularly suitable for generating trajectories in real-time for safe operations in complex environments and multiple vehicle missions. We show, using a rigorous mathematical framework, that the solution to the discretized optimal motion planning problem converges to that of the continuous-time one. The advantages of the proposed method are investigated through numerical examples.

Approximation methods

Autonomous vehicles

Bernstein polynomial

Bezier curve

Convergence

multiple vehicles

Optimal control

optimal motion planning

Planning

Tools

Trajectory

Details

Title: Subtitle: Optimal Multivehicle Motion Planning Using Bernstein Approximants
Creators: Venanzio Cichella - University of Iowa
Isaac Kaminer - Naval Postgraduate School
Claire Walton - Naval Postgraduate School
Naira Hovakimyan - University of Illinois at Urbana–Champaign
Antonio M Pascoal - University of Lisbon
Resource Type: Journal article
Publication Details: IEEE transactions on automatic control, Vol.66(4), pp.1453-1467
Publisher: IEEE
DOI: 10.1109/TAC.2020.2999329
ISSN: 0018-9286
eISSN: 1558-2523
Grant note: GA 731103 / H2020 EUMR Research Infrastructure Network N00014-19-1-2106; N00014-19-WX0-0155 / ONR FA9550-15-1-0518 / Air Force Office of Scientific Research; AFOSR (10.13039/100000181)
Language: English
Date published: 04/2021
Academic Unit: Mechanical Engineering
Record Identifier: 9984196606902771

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