Game theory-based negotiation for multiple robots task allocation

Abstract

This paper investigates task allocation for multiple robots by applying the game theory-based negotiation approach. Based on the initial task allocation using a contract net-based approach, a new method to select the negotiation robots and construct the negotiation set is proposed by employing the utility functions. A negotiation mechanism suitable for the decentralized task allocation is also presented. Then, a game theory-based negotiation strategy is proposed to achieve the Pareto-optimal solution for the task reallocation. Extensive simulation results are provided to show that the task allocation solutions after the negotiation are better than the initial contract net-based allocation. In addition, experimental results are further presented to show the effectiveness of the approach presented.

Efficient multi-robot formations using distributed optimization

“…Equation (11) can be obtained by deriving (10) and making it equal to zero. Once q 1 is available, the rest of the positions are obtained using (9).…”

“…An early example of distributed solution is [8], although the communication graph is implicitly complete. Game-theory is used in a distributed framework in [9]. The problem of the optimal assignment together with collisionfree trajectories is studied in [10].…”

A contract-net model for foraging agents in dynamic environment

“…A threshold-limited load balance strategy for awarding agents is studied in [9]. Optimal resource usage among a team of agents [10], cooperative control of multi-robots [11], task coordination on a physical multi mobile robot platform [12], cooperation among multiple vacuum cleaning robots [13], adaptive bidding using artificial neural networks for foraging robots [14], task allocation using game theory for multiple robots [15] are some studies related with Contract-Net based task allocation for multi-agent systems.…”

Increasing allocated tasks with a time minimization algorithm for a search and rescue scenario

“…[10] extends the CBBA to permit asynchronous communication, while [11] developed a strategy based on the CBBA and the algorithm from [12], for assigning critical tasks where agents have limited capacity. [13] propose an alternative method using a game theory approach for resolving conflicts. Most relevant to this paper is the approach in [14], which introduces the concept of a significance value of a task.…”