As autonomous personal robots come of age, we expect certain applications to be executed with a high degree of repeatability and robustness. In order to explore these applications and their challenges, we need tools and strategies that allow us to develop them rapidly. Serving drinks (i.e., locating, fetching, and delivering), is one such application with well-defined environments for operation, requirements for human interfacing, and metrics for successful completion. In this paper we present our experiences and results while building an autonomous robotic assistant using the PR21 platform and ROS2. The system integrates several new components that are built on top of the PR2's current capabilities. Perception components include dynamic obstacle identification, mechanisms for identifying the refrigerator, types of drinks, and human faces. Planning components include navigation, arm motion planning with goal and path constraints, and grasping modules. One of the main contributions of this paper is a new task-level executive system, SMACH, based on hierarchical concurrent state machines, which controls the overall behavior of the system. We provide in-depth discussions on the solutions that we found in accomplishing our goal, and the implementation strategies that let us achieve them.

We describe an autonomous robotic system capable of navigating through an office environment, opening doors along the way, and plugging itself into electrical outlets to recharge as needed. We demonstrate through extensive experimentation that our robot executes these tasks reliably, without requiring any modification to the environment. We present robust detection algorithms for doors, door handles, and electrical plugs and sockets, combining vision and laser sensors. We show how to overcome the unavoidable shortcoming of perception by integrating compliant control into manipulation motions. We present a visual-differencing approach to high-precision plug-insertion that avoids the need for high-precision hand-eye calibration.

When developing path tracking controllers for autonomous vehicles the dynamic constraints of the vehicle are a critical factor. It is therefore necessary to ensure that all tracking trajectories produced by the controller are smooth and continuous. In this paper, a path tracking controller is proposed and implemented on an experimental autonomous vehicle. This tracking method decouples the low-level heading and steering control of the vehicle from the main tracking controller, therefore requiring less vehicle characterization. The results of this paper will show this method yields a RMS 0.25m cross-track error with little to no vehicle characterization.