Book Description

This thesis investigates autonomous and fault-tolerant cooperative operation and intelligent control of multi-robot systems in a dynamic, unstructured, and unknown environment. It makes significant original contributions pertaining to autonomous robot cooperation, dynamic task allocation, system robustness, and real-time performance. The thesis develops a fully autonomous and fault tolerant distributed control system framework based on an artificial immune system for cooperative multi-robot systems. The multi-robot system consists of a team of heterogeneous mobile robots which cooperate with each other to achieve a global goal while resolving conflicts and accommodating full and partial failures in the robots. In this framework, the system autonomously chooses the appropriate number of robots required for carrying out the task in an unknown and unpredictable environment. An artificial immune system (AIS) approach is incorporated into the multi-robot system framework, which will provide robust performance, self-deterministic cooperation, and coping with an inhospitable environment. Based on the structure of the human immune system, immune response, immune network theory, and the mechanisms of interaction among antibody molecules, the robots in the team make independent decisions, coordinate, and if required cooperate with each other to accomplish a common goal. As needed for application in cooperative object transportation by mobile robots, the thesis develops a new method of object pose estimation. In this method, a CCD camera, optical encoders, and a laser range finder are the sensors used by the robots to estimate the pose of the detected object. The thesis also develops a market-based algorithm for autonomous multi-robot cooperation, which is then used for comparative evaluation of the performance of the developed AIS-based system framework. In order to validate the developed techniques, a Java-based simulation system and a physical multi-robot experimental syste.