Memetic algorithms for optimal task allocation in multi-robot systems for inspection problems with cooperative tasks

• Published in: Soft computing (Berlin, Germany) Vol. 19; no. 3; pp. 567 - 584
• Main Authors: Liu, Chun, Kroll, Andreas
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2015; Springer Nature B.V
• Subjects: Artificial Intelligence; Computational Intelligence; Control; Engineering; Genetic algorithms; Inspection; Mathematical Logic and Foundations; Mechatronics; Methodologies and Application; Multiple robots; Optimization; Permutations; Petroleum refining; Refineries; Robotics; Robots; Storage tanks; Task complexity; Task scheduling; Inspection; Memetic algorithm; Genetic algorithm; Constrained combinatorial optimization; Cooperation; Multi-robot task allocation
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-014-1274-0

Multi-robot task allocation means to distribute and schedule a set of tasks to be accomplished by a group of robots to minimize cost while satisfying operational constraints. It can be challenging to solve a large number of tasks and becomes even more challenging when tightly coupled multi-robot tasks are also taken into account. For example, it is more complex to solve problems that include tasks that have to be carried out jointly by two robots due to the resulting temporal and spatial constraints. Additionally, the complexity of task allocation increases exponentially with rising task variety. This paper focuses on multi-robot task allocation in inspection problems with both single- and two-robot tasks. A novel memetic algorithm is proposed combining a genetic algorithm with two local search schemes. Using permutation representation, eight approaches based on four basic coding strategies are designed for multi-robot task allocation of inspection problems with two-robot tasks. The performance of the memetic algorithm is evaluated in case studies on inspecting a large storage tank area of a petroleum refinery.