proposed multiagent systems to share the rewards and penalties of successes and failures [3].Almost all the chess game can be described by game tree. Game tree presents the possible movements and lists all situations for the chess. In the paper, we want
to use the game tree to program the motion trajectories of the Chinese chess piece, and use the multi-robot to present the scenario of the movement for the Chinese
chess game. Many researchers have been studying the problem of multi-robot task allocation for some time. In general, the task allocation problem divides a task into
many subtasks, and assigns some robots to each
subtask. Gerkey addresses the multiple robot –multiple
task problem (MR-MT) [4] where the object is to
assign a robot team to multiple task. So that the
systems’ efficiency is maximize. This problem is also
called the coalition formation. Gerkey and Mataric [5]
indicate that despite the existence of various multiagent
coalition algorithms. These algorithms have not
been demonstrated in the multi-robot domain. Vig and
Julie show that, with certain modifications, coalition
formation algorithms provided in the multi-agent
domain can be applied to the multi-robot domain [6].
Chen and Li have been proposed a power-efficient path
planning protocol named collaborative path planning
algorithm (CPPA) for a multi-robot system without
global positioning system (GPS) [7].
The paper is organized as follows: Section II
describes the system structure of the multiple robot
based Chinese chess game system. Section III presents
the function of the mobile robot. Section IV explains
the evaluation method of the Chinese chess game using
multiple mobile robots, and the experimental results
are implemented in section V. Section VI presents
brief concluding comments.