Most mobile robots in existence today have large, heavy, statically stable bases. To enable manipulation, actuated arms and effectors are attached to the base providing limited postural control. On the other hand, some of the latest developments in humanoid robotics have succeeded in reducing the footprint and raising the center of mass of a robot using whole body postural control and balancing. By comparing the magnitude of various forces that can be applied to the environment, we demonstrate the advantages of whole body postural control. The results presented suggest that for pushing, pulling, or carrying types of tasks, using whole body postural control can lead to higher performance by allowing a platform to apply more force to the environment

In this paper we propose a new approach for multi-objective control using a cascade of filters that progressively removes candidate commands which do not satisfy task constraints. The approach is motivated by other control methods that prevent destructive control interactions through null space projections. We apply this approach to a practical leader/follower task in which a mobile robot must address the conflicting objectives of moving to a goal position while avoiding obstacles and keeping a region of the workspace within the field of view of a fixed camera mounted on the platform. We experimentally verify our approach using the Segway Robotic Mobility Platform (RMP), a dynamically stable, differential drive mobile robot.

A set of impedance controllers is used for both state estimation and tracking control on a mobile robot. State estimation is based on the states of a family of impedance controllers and tracking is implemented through a single controller from this set. Reinforcement learning techniques are used to create switching policies that optimize time or energy in a path tracking task.