Abstract
AbstractBecause of high speed, efficiency, robustness and flexibility of multi-agent systems, in recent years there has been an increasing interest in the art of these systems. Artificial market mechanisms are one of the well-known negotiation multi-agent protocols in multi-agent systems. In this paper artificial capital market as a new variant of market mechanism is introduced and employed in a multi-robot foraging problem. In this artificial capital market, the robots are going to benefit via investment on some assets, defined as doing foraging task. Each investment has a cost and an outcome. Limited initial capital of the investors constrains their investments. A negotiation protocol is proposed for decision making of the agents. Qualitative analysis reveals speed of convergence, near optimal solutions and robustness of the algorithm. Numerical analysis shows advantages of the proposed method over two previously developed heuristics in terms of four performance criteria.
Highlights
Swarm robotics is a state of the art in robotics research
According to Ref 34, four local heuristics have been used in the literature: x Closest Task First (CT): In this heuristic each robot selects an available task that is closest to it. x Most Starved Task First (MST): In this heuristic, in order to balance the task load across the environment, the robots are more likely to perform the tasks those have the least number of robots in their neighborhood and necessitate more robots to complete them. x Most Starved, Most Complete Task First (MSMCT): This is an extension of MST where the number of robots, required to complete a task, is taken in account
Assuming that all robots those are located inside the achievable area contribute in carrying the object we are going to find expected averages of number of robots contributing in foraging, steps taken by robots and fuel consumption by robots
Summary
Swarm robotics is a state of the art in robotics research. Large group of robots, when working with together, potentially can emerge complicated behaviors. The reason is that foraging comprises some interesting and sophisticated sub-problems such as energy efficiency[2,3,4,5,6], path and motion planning[7,8,9,10,11,12], coordination[10, 13,14,15,16], communication[17,18,19,20,21,22,23,24,25], optimization[3, 79, 26,27,28,29,30,31] and task allocation[32,33,34,35,36,37,38,39,40,41,42,43]. We will use artificial capital market as a task allocation mechanism in a multi-robot foraging system.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
