Multi-Robot Cooperative Task Allocation With Definite Path-Conflict-Free Handling

Abstract

Modeling and solving multi-robot task allocation with definite path-conflict-free handling is an important research, especially in real working environments. Some of the research lines are unable to obtain definite path-conflict-free solutions for multi-robot task allocations, such as using the penalty-term method in the fitness function to restrict the survival probabilities of the solutions with path conflicts. In some cases, these solutions are only able to satisfy the objective of minimizing task time. We formulate this problem based on grid maps, while focusing on the frequently used cooperative task allocation. In our model, two subtasks of each cooperative task must be executed by two robots, simultaneously. We propose vitality-driven genetic task allocation algorithm (VGTA), which is able to simultaneously minimize task time and realize definite conflict-free path planning. VGTA consists of local operators, such as random mutations, greedy crossovers, and vitality selection. Meanwhile, VGTA includes schedule conflict and path conflict handling strategies. In path conflict handling strategy, we not only consider the common path conflicts in a grid cell, but also focus on the path conflicts between robots when exchanging positions in the adjacent grid cells. Besides, we construct our benchmarks based on real working environments, such as factory, powerhouse, and airport environments. Experimental results indicate that VGTA's search capability and computation cost are satisfactory. Meanwhile, its solutions are able to be really executed.INDEX TERMS Multi-robot task allocation, cooperative task, schedule conflict, path conflict, unmanned multi-robot swarm.

Cooperative Task Allocation for Multi-Robot Systems Based on Multi-Objective Ant Colony System

“…f 1 (P) λf 2 (P) (10) where x ijk equals to 1 if vehicle i performs task k after finishing j and 0 otherwise, c (i) jk (P i ) is the cost of vehicle i from task j to k along task path P i , which is defined in (1). Equations ( 3) and ( 4) represent the total and maximum cost of the vehicles, respectively.…”

“…The time cost of vehicle i from task j to k is defined as (1), which refers to the sum of the cruising time from the former task to the latter one and executing time of the former task. Similarly, given any µ in [0, 1], the time cost can also be defined as…”

“…In this section, three values of µ were considered. µ = 0, which is the same as (1). µ = 0.5, which represents that the cost is defined as the sum of the cruising time and half of the executing time of both the former and latter tasks.…”

“…Research on multi-robot systems has drawn significant interest as it is capable of performing complicated and complex tasks more effectively and efficiently, and it is more faulttolerant compared with a single robot [1], [2]. In order to develop and deploy multi-robot systems in real-world applications, one of the most critical problems is task allocation, which aims to coordinate the robotic vehicles to perform tasks to optimize one or more objectives [3]- [5].…”

Cloud-Based Cyber-Physical Robotic Mobile Fulfillment Systems: A Case Study of Collision Avoidance

“…A conflict prognosis will promptly detect related collisions and adopt specific collision avoidance approaches to solve the competition circumstances. Mobile robots are assumed to be fully equipped to complete customer orders, and the avoidance of collisions, such as the stay-on collision, cross collision, node-occupancy collision, shelf-occupancy collision, and newly collision that appeared in RMFS, is vital for nearly real-time checks and responses in the physical environment [13], [21], [110]- [112]. 3) Path Planning: The path planning problem in RMFS requires multi-robot cooperation.…”