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Action module planning and Cartesian based control of an experimental climbing robot

This thesis presents the application of an action module planning methodology and a simplified cartesian computed torque (SCCT) control scheme to an experimental climbing robot named LIBRA. The action module planning method is based on physically realizable actions by the robot, called action modules, which placed in the correct sequence will produce a plan for successful execution of a given task. A genetic algorithm based search technique is used to find a plan with the correct sequence of action modules such that the task is executed without the violation of any physical constraints of the robot. The SCCT controller is developed, based on some assumptions associated with highly geared mobile robots, for the control of these systems in unknown or partially known environments. These assumptions are validated through both simulation and experimental results using the LIBRA robot. The SCCT control scheme is shown to have improved performance, over traditional Jacobian transpose control, for the LIBRA multi-limbed robot. Finally, the action module planning methodology and SCCT control scheme are applied to the experimental climbing robot. The results obtained from this study suggest that the planning and control methods allow robots to function and execute tasks in difficult environments. Thesis Supervisor: Dr. Steven Dubowsky Professor of Mechanical Engineering