In this study, a mixed-integer programming formulation is developed for a team of homogeneous sensing agents under a bi-objective optimization framework to solve a discrete open-loop centralized multi-agent search and rescue path planning problem. The first objective represents the maximization of probability of target detection to ensure the success of mission planning and the second objective represents minimization of the cumulative path length of all the agents to ensure resource utilization and ensure adequate area coverage. A two-phase fuzzy programming technique is used to find the Pareto optimal solution. Numerical experiments are conducted with CPLEX to evaluate the effectiveness of the solution procedure with varying number of agents, and the impact of the size of a grid-based rectangular map with a sparsely distributed non-cooperative finite number of stationary targets.
|Title of host publication||Distributed Computing and Artificial Intelligence, Special Sessions, 17th International Conference, DCAI 2020|
|Editors||Sara Rodríguez González, Javier Prieto, Alfonso González-Briones, Arkadiusz Gola, George Katranas, Michela Ricca, Roussanka Loukanova, Roussanka Loukanova|
|Number of pages||10|
|Publication status||Published - 2021|
|Event||17th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2020 - L´Aquila, Italy|
Duration: 17 Jun 2020 → 19 Jun 2020
|Conference||17th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2020|
|Period||17/06/2020 → 19/06/2020|
|Series||Advances in Intelligent Systems and Computing|
Bibliographical notePublisher Copyright:
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.
Copyright 2020 Elsevier B.V., All rights reserved.
- Mixed-integer programming
- Multi-agent path planning
- Search and rescue