5 research outputs found
Multi-Agent Task Allocation in Complementary Teams: A Hunter and Gatherer Approach
Consider a dynamic task allocation problem, where tasks are unknowingly
distributed over an environment. This paper considers each task comprised of
two sequential subtasks: detection and completion, where each subtask can only
be carried out by a certain type of agent. We address this problem using a
novel nature-inspired approach called "hunter and gatherer". The proposed
method employs two complementary teams of agents: one agile in detecting
(hunters) and another skillful in completing (gatherers) the tasks. To minimize
the collective cost of task accomplishments in a distributed manner, a
game-theoretic solution is introduced to couple agents from complementary
teams. We utilize market-based negotiation models to develop incentive-based
decision-making algorithms relying on innovative notions of "certainty and
uncertainty profit margins". The simulation results demonstrate that employing
two complementary teams of hunters and gatherers can effectually improve the
number of tasks completed by agents compared to conventional methods, while the
collective cost of accomplishments is minimized. In addition, the stability and
efficacy of the proposed solutions are studied using Nash equilibrium analysis
and statistical analysis respectively. It is also numerically shown that the
proposed solutions function fairly, i.e. for each type of agent, the overall
workload is distributed equally.Comment: 15 pages, 12 figure
Routing with Face Traversal and Auctions Algorithms for Task Allocation in WSRN
International audienceFour new algorithms (RFTA1, RFTA2, GFGF2A, and RFTA2GE) handling the event in wireless sensor and robot networks based on the greedy-face-greedy (GFG) routing extended with auctions are proposed in this paper. In this paper, we assume that all robots are mobile, and after the event is found (reported by sensors), the goal is to allocate the task to the most suitable robot to act upon the event, using either distance or the robots' remaining energy as metrics. The proposed algorithms consist of two phases. The first phase of algorithms is based on face routing, and we introduced the parameter called search radius (SR) at the end of this first phase. Routing is considered successful if the found robot is inside SR. After that, the second phase, based on auctions, is initiated by the robot found in SR trying to find a more suitable one. In the simulations, network lifetime and communication costs are measured and used for comparison. We compare our algorithms with similar algorithms from the literature (k-SAAP and BFS) used for the task assignment. RFTA2 and RFTA2GE feature up to a seven-times-longer network lifetime with significant communication overhead reduction compared to k-SAAP and BFS. Among our algorithms, RFTA2GE features the best robot energy utilization