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Analysis of path following and obstacle avoidance for multiple wheeled robots in a shared workspace

Published online by Cambridge University Press:  31 August 2018

M. Hassan Tanveer*
Affiliation:
Department of Informatics, Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genova, Via Opera Pia 13, 16145, Italy. Emails: [email protected], [email protected]
Carmine T. Recchiuto
Affiliation:
Department of Informatics, Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genova, Via Opera Pia 13, 16145, Italy. Emails: [email protected], [email protected]
Antonio Sgorbissa
Affiliation:
Department of Informatics, Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genova, Via Opera Pia 13, 16145, Italy. Emails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]

Summary

The article presents the experimental evaluation of an integrated approach for path following and obstacle avoidance, implemented on wheeled robots. Wheeled robots are widely used in many different contexts, and they are usually required to operate in partial or total autonomy: in a wide range of situations, having the capability to follow a predetermined path and avoiding unexpected obstacles is extremely relevant. The basic requirement for an appropriate collision avoidance strategy is to sense or detect obstacles and make proper decisions when the obstacles are nearby. According to this rationale, the approach is based on the definition of the path to be followed as a curve on the plane expressed in its implicit form f(x, y) = 0, which is fed to a feedback controller for path following. Obstacles are modeled through Gaussian functions that modify the original function, generating a resulting safe path which – once again – is a curve on the plane expressed as f′(x, y) = 0: the deformed path can be fed to the same feedback controller, thus guaranteeing convergence to the path while avoiding all obstacles. The features and performance of the proposed algorithm are confirmed by experiments in a crowded area with multiple unicycle-like robots and moving persons.

Type
Articles
Copyright
Copyright © Cambridge University Press 2018 

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