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Mission Aware Motion Planning (MAP) Framework with Physical and Geographical Constraints for a Swarm of Mobile Stations

Harikumar, K and Senthilnath, J and Sundaram, S (2020) Mission Aware Motion Planning (MAP) Framework with Physical and Geographical Constraints for a Swarm of Mobile Stations. In: IEEE Transactions on Cybernetics, 50 (3). pp. 1209-1219.

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Official URL: https://doi.org/10.1109/TCYB.2019.2897027


In this paper, we propose a mission aware motion planning (MAP) framework for a swarm of autonomous unmanned ground vehicles (UGVs) or mobile stations in an uncertain environment for efficient supply of resources/services to unmanned aerial vehicles (UAVs) performing a specific mission. The MAP framework consists of two levels, namely, centralized mission planning and decentralized motion planning. On the first level, the centralized mission planning algorithm estimates the density of UAV in a given environment for determining the number of UGVs and their initial operating location. In the subsequent level, a decentralized motion planning algorithm which provides a closed-form expression for velocity command using adaptive density estimation has been proposed. Further, the physical and geographical constraints are integrated into motion planning. A Monte-Carlo simulation is performed to evaluate the advantages of the MAP over distributed stationary stations (DSSs) often used in the literature. The obtained results clearly indicate that in comparison with DSS, MAP reduces the average distance traveled by UAVs about 20%, reduces the loss of mission time by 90 s per interruption and power loss by 3 dB

Item Type: Journal Article
Publication: IEEE Transactions on Cybernetics
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: The copyright for this article belongs to the Institute of Electrical and Electronics Engineers Inc.
Keywords: Antennas; Collision avoidance; Ground vehicles; Intelligent systems; Intelligent vehicle highway systems; Monte Carlo methods; Unmanned aerial vehicles (UAV), Average Distance; Closed-form expression; Density estimation; mission awareness; Mission planning; Motion planning algorithms; Uncertain environments; Unmanned ground vehicles, Motion planning, algorithm; article; Monte Carlo method; motion; unmanned aerial vehicle; velocity
Department/Centre: Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited: 02 Feb 2023 04:50
Last Modified: 02 Feb 2023 04:50
URI: https://eprints.iisc.ac.in/id/eprint/79703

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