Search Methods for Mobile Manipulator Performance Measurement

Mobile manipulators are a potential solution to the increasing need for additional flexibility and mobility in industrial robotics applications. However, they tend to lack the accuracy and precision achieved by fixed manipulators, especially in scenarios where both the manipulator and the autonomous vehicle move simultaneously. This thesis analyzes the problem of dynamically evaluating the positioning error of mobile manipulators. In particular, it investigates the use of Bayesian methods to predict the position of the end-effector in the presence of uncertainty propagated from the mobile platform. Simulations and real-world experiments are carried out to test the proposed method against a deterministic approach. These experiments are carried out on two mobile manipulators - a proof-of-concept research platform and an industrial mobile manipulator - using ROS and Gazebo. The precision of the mobile manipulator is evaluated through its ability to intercept retroreflective markers using a photoelectric sensor attached to the end-effector. Compared to the deterministic search approach, we observed improved interception capability with comparable search times, thereby enabling the effective performance measurement of the mobile manipulator