This thesis presents a new approach for selecting a manipulator arm configuration (a pose) in an environment where the positions of the work items are not able to be fully controlled. The approach utilizes a belief formed from a priori knowledge, observations and predictive models to select manipulator poses and motions. Standard methods for manipulator control provide a fully specified Cartesian pose as the input to a robot controller which is assumed to act as an ideal Cartesian motion device. While this approach simplifies the controller and makes it more portable, it is not well suited for less-controlled environments where the work item position or orientation may not be completely observable and where a measure of the accuracy of the available observations is required. The proposed approach suggests selecting a manipulator configuration using two types of rating function. When uncertainty is high, configurations are rated by combining a belief, represented by a probability density function, and a value function in a decision theoretic manner enabling selection of the sensor??s motion based on its probabilistic contribution to information gain. When uncertainty is low the mean or mode of the environment state probability density function is utilized in task specific linear or angular distances constraints to map a configuration to a cost. The contribution of this thesis is in providing two formulations that allow joint configurations to be found using non-linear optimization algorithms. The first formulation shows how task specific linear and angular distance constraints are combined in a cost function to enable a satisfying pose to be selected. The second formulation is based on the probabilistic belief of the predicted environment state. This belief is formed by utilizing a Bayesian estimation framework to combine the a priori knowledge with the output of sensor data processing, a likelihood function over the state space, thereby handling the uncertainty associated with sensing in a less controlled environment. Forward models are used to transform the belief to a predicted state which is utilized in motion selection to provide the benefits of a feedforward control strategy. Extensive numerical analysis of the proposed approach shows that using the fed-forward belief improves tracking performance by up to 19%. It is also shown that motion selection based on the dynamically maintained belief reduces time to target detection by up to 50% compared to two other control approaches. These and other results show how the proposed approach is effectively able to utilize an uncertain environment state belief to select manipulator arm configurations.

This thesis presents a new approach for selecting a manipulator arm configuration (a pose) in an environment where the positions of the work items are not able to be fully controlled. The approach utilizes a belief formed from a priori knowledge, observations and predictive models to select manipulator poses and motions. Standard methods for manipulator control provide a fully specified Cartesian pose as the input to a robot controller which is assumed to act as an ideal Cartesian motion device. While this approach simplifies the controller and makes it more portable, it is not well suited for less-controlled environments where the work item position or orientation may not be completely observable and where a measure of the accuracy of the available observations is required. The proposed approach suggests selecting a manipulator configuration using two types of rating function. When uncertainty is high, configurations are rated by combining a belief, represented by a probability density function, and a value function in a decision theoretic manner enabling selection of the sensor??s motion based on its probabilistic contribution to information gain. When uncertainty is low the mean or mode of the environment state probability density function is utilized in task specific linear or angular distances constraints to map a configuration to a cost. The contribution of this thesis is in providing two formulations that allow joint configurations to be found using non-linear optimization algorithms. The first formulation shows how task specific linear and angular distance constraints are combined in a cost function to enable a satisfying pose to be selected. The second formulation is based on the probabilistic belief of the predicted environment state. This belief is formed by utilizing a Bayesian estimation framework to combine the a priori knowledge with the output of sensor data processing, a likelihood function over the state space, thereby handling the uncertainty associated with sensing in a less controlled environment. Forward models are used to transform the belief to a predicted state which is utilized in motion selection to provide the benefits of a feedforward control strategy. Extensive numerical analysis of the proposed approach shows that using the fed-forward belief improves tracking performance by up to 19%. It is also shown that motion selection based on the dynamically maintained belief reduces time to target detection by up to 50% compared to two other control approaches. These and other results show how the proposed approach is effectively able to utilize an uncertain environment state belief to select manipulator arm configurations.