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Control of mobile manipulation with networked sensing

University of British Columbia

This thesis addresses the manipulation control of a mobile robot with the support of a sensor network, for carrying out dynamically challenging tasks. Such tasks are defined as those where the robot is required to first identify objects, approach and grasp the needed objects, and transport them to goal locations in an environment that is dynamic, unstructured and only partially known. In the present work, a robotic system with these capabilities is developed and implemented for use in tasks of search and rescue, and homecare robotics. To this end, this thesis makes significant original contributions in developing a scheme of adaptive nonlinear model predictive control (ANMPC) and a sensor network with dynamic clustering capability for mobile manipulation under challenging conditions. Two object tracking algorithms for color tracking and feature tracking are developed for object identification and tracking. A system that uses Q-learning is developed for mobile robot navigation, which allows the robot to learn and operate in an unknown and unstructured dynamic environment. A traditional approach of image-based visual servo control is developed and demonstrated. The scheme of ANMPC is developed, which incorporates a multi-input multi-output (MIMO) control system that can accommodate constraints, including environmental constraints and physical constraints of the robots. In implementing ANPC scheme, the nonlinear and time-variant model is linearized on line with respect to the current position of the image feature and robot joints, using an adaptive approach. The corresponding control architecture predicts the system outputs and generates optimized control actions according to a cost function. In order to extend the mobile manipulation system to a wider workspace such as that found in cities and home scenarios, a sensor network is designed and developed employing PFSA (Probabilistic Finite State Automata). The developed PFSA is utilized in both modeling the sensor data and organizing and representing the sensor network. An application of object identification and tracking is presented; and a heterogeneous sensor network is developed along with a simulation platform in MATLAB. A self-organized and clustered sensor network, which is based on PFSA, is demonstrated. In conclusion, directions for further research and development are indicated.

Text

2012-09-27

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/43277

Mechanical Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/