A Cooperative Ant Colony System for Multiobjective Multirobot Task Allocation With Precedence Constraints

In many real-world scenarios (e.g., product manufacturing), multiple heterogeneous robots cooperate to complete complex tasks with precedence constraints. In these heterogeneous multirobot systems, the multirobot task allocation problem is important and has attracted increasing attention. The problem usually involves multiple optimization objectives for decision making. However, existing approaches meet challenges on multi-objective problems with large-scale tasks and precedence constraints in terms of solution diversity and convergence. Therefore, this paper formulates a tri-objective model and proposes a cooperative ant colony system (CACS) to optimize three objectives, i.e., minimizing the makespan, average robot traveling time, and average task waiting time. In CACS, three ant colonies are created to simultaneously optimize the three objectives. To coordinate with the precedence constraints of the problem, solutions are encoded as a task-alliance sequence. A new solution construction method is developed to generate feasible solutions using dynamic heuristic information and two pheromone matrices. Particularly, one matrix deposits pheromone between tasks for task selection and the other between tasks and robots for alliance building. To further improve solution diversity and convergence, a fusion-based local search is adopted to generate high-quality solutions by combining information from multiple colonies. Thirty instances are constructed with different numbers of tasks and robots under complex precedence constrains. Experimental results show that CACS outperforms state-of-the-art methods in terms of the inverted generational distance and hypervolume metrics.