TY - GEN
T1 - Path Planning and Task Assignment for Data Retrieval from Wireless Sensor Nodes Relying on Game-Theoretic Learning
AU - Papatheodorou, Sotiris
AU - Smyrnakis, Michalis
AU - Hamidou, Tembine
AU - Tzes, Anthony
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/6/22
Y1 - 2018/6/22
N2 - The energy-efficient trip allocation of mobile robots employing differential drives for data retrieval from stationary sensor locations is the scope of this article. Given a team of robots and a set of targets (wireless sensor nodes), the planner computes all possible tours that each robot can make if it needs to visit a part of or the entire set of targets. Each segment of the tour relies on a minimum energy path planning algorithm. After the computation of all possible tour-segments, a utility function penalizing the overall energy consumption is formed. Rather than relying on the NP-hard Mobile Element Scheduling (MES) MILP problem, an approach using elements from game theory is employed. The suggested approach converges fast for most practical reasons thus allowing its utilization in near real time applications. Simulations are offered to highlight the efficiency of the developed algorithm.
AB - The energy-efficient trip allocation of mobile robots employing differential drives for data retrieval from stationary sensor locations is the scope of this article. Given a team of robots and a set of targets (wireless sensor nodes), the planner computes all possible tours that each robot can make if it needs to visit a part of or the entire set of targets. Each segment of the tour relies on a minimum energy path planning algorithm. After the computation of all possible tour-segments, a utility function penalizing the overall energy consumption is formed. Rather than relying on the NP-hard Mobile Element Scheduling (MES) MILP problem, an approach using elements from game theory is employed. The suggested approach converges fast for most practical reasons thus allowing its utilization in near real time applications. Simulations are offered to highlight the efficiency of the developed algorithm.
KW - Distributed Optimization
KW - Game-Theoretic Learning.
KW - Multi-Agent Systems
KW - Robotic Data Mule
KW - Wireless Sensor Networks
UR - http://www.scopus.com/inward/record.url?scp=85050213798&partnerID=8YFLogxK
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U2 - 10.1109/CoDIT.2018.8394924
DO - 10.1109/CoDIT.2018.8394924
M3 - Conference contribution
AN - SCOPUS:85050213798
T3 - 2018 5th International Conference on Control, Decision and Information Technologies, CoDIT 2018
SP - 1073
EP - 1078
BT - 2018 5th International Conference on Control, Decision and Information Technologies, CoDIT 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Control, Decision and Information Technologies, CoDIT 2018
Y2 - 10 April 2018 through 13 April 2018
ER -