A Connectivity-Prediction Algorithm and its Application in Active Cooperative Localization for Multi-Robot Systems
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Date
2020
Publication Type
Conference Paper
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Abstract
This paper presents a method for predicting the probability of future connectivity between mobile robots with range-limited communication. In particular, we focus on its application to active motion planning for cooperative localization (CL). The probability of connection is modeled by the distribution of quadratic forms in random normal variables and is computed by the infinite power series expansion theorem. A finite-term approximation is made to realize the computational feasibility and three more modifications are designed to handle the adverse impacts introduced by the omission of the higher order series terms. On the basis of this algorithm, an active and CL problem with leader-follower architecture is then reformulated into a Markov Decision Process (MDP) with a one-step planning horizon, and the optimal motion strategy is generated by minimizing the expected cost of the MDP. Extensive simulations and comparisons are presented to show the effectiveness and efficiency of both the proposed prediction algorithm and the MDP model. © 2020 IEEE.
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published
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Book title
2020 IEEE International Conference on Robotics and Automation (ICRA)
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Pages / Article No.
9824 - 9830
Publisher
IEEE
Event
IEEE International Conference on Robotics and Automation (ICRA 2020)
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Organisational unit
03737 - Siegwart, Roland Y. / Siegwart, Roland Y.
03737 - Siegwart, Roland Y. / Siegwart, Roland Y.
Notes
Due to the Coronavirus (COVID-19) the conference was conducted virtually.