We present a multi-mobile robot collision avoidance system based on the velocity obstacle paradigm. Current positions and velocities of surrounding robots are translated to an efficient geometric representation to determine safe motions. Each robot uses on-board localization and local communication to build the velocity obstacle representation of its surroundings. Our close and error-bounded convex approximation of the localization density distribution results in collision-free paths under uncertainty. While in many algorithms the robots are approximated by circumscribed radii, we use the convex hull to minimize the overestimation in the footprint. Results show that our approach allows for safe navigation even in densely packed environments.

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 PR21platform 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 algorithm for detection of electrical outlets in images obtained by a monocular camera. We provide a method for calculating 3D coordinates of outlet holes with accuracy high enough for a robot to plug in without visual servoing. The paper proposes a novel algorithm for accurate pose estimation of a small planar object. We use the plane normal obtained from stereo data as a hard constraint for a planar PnP problem. A different cost function is proposed together with a closed-form solution for the object pose. This system is used for continuous operation of a PR2 robot enabling it to run for 2 weeks without human supervision.

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.

This paper presents a contribution to programming by human demonstration, in the context of compliant-motion task specification for sensor-controlled robot systems that physically interact with the environment. One wants to learn about the geometric parameters of the task and segment the total motion executed by the human into subtasks for the robot, that can each be executed with simple compliant-motion task specifications. The motion of the human demonstration tool is sensed with a 3-D camera, and the interaction with the environment is sensed with a force sensor in the human demonstration tool. Both measurements are uncertain, and do not give direct information about the geometric parameters of the contacting surfaces, or about the contact formations (CFs) encountered during the human demonstration. The paper uses a Bayesian sequential Monte Carlo method (also known as a particle filter) to do the simultaneous estimation of the CF (discrete information) and the geometric parameters (continuous information). The simultaneous CF segmentation and the geometric parameter estimation are helped by the availability of a contact state graph of all possible CFs. The presented approach applies to all compliant-motion tasks involving polyhedral objects with a known geometry, where the uncertain geometric parameters are the poses of the objects. This work improves the state of the art by scaling the contact estimation to all possible contacts, by presenting a prediction step based on the topological information of a contact state graph, and by presenting efficient algorithms that allow the estimation to operate in real time. In real-world experiments, it is shown that the approach is able to discriminate in real time between some 250 different CFs in the graph

In this paper a novel formalism to characterize contact states between an articulated polyhedral object and a polyhedral environment for the generation of the graph of feasible contact states between them is presented. This formalism is based upon a particular representation of the stratification of the configuration space of the articulated object by means of oriented matroid theory. A stratification is a decomposition of a set into a collection of manifolds which in our case correspond to the different contact states between the articulated object and the environment. In the representation of the stratification of the configuration space using oriented matroid theory the topological properties of the different strata are represented at a purely combinatorial level. An algorithm to enumerate the existing strata and to find the adjacency relationships among them is proposed. It will be shown that the symbolic computation based on oriented matroids simplifies and in some cases even replaces the computation with coordinates.

This paper presents the compliant task generator: a new approach for the automatic conversion of a geometric path generated by a compliant path planner to a force based task specification for a compliant robot controller. Based on the geometric model of a moving object and its environment, a compliant path planner generates a set of six-dimensional positions x/sub 1...m/ and their corresponding contact formations CF/sub 1...n/. The compliant force controller, which executes a planned path under force feedback using the hybrid control paradigm, expects a desired force w/sub d/, velocity t/sub d/ and position x/sub d/ at each time-step, together with their force and velocity controlled subspaces W and T. To specify these controller primitives, we add information about the desired dynamic interaction between the moving object and its environment, in the form of the desired kinetic energy E/sub kin/ of the moving object and the potential energy E/sub pot/ in the contacts with the environment, together with the inertia and stiffness matrix M and S. We fully automated the conversion process of the compliant planner output together with the added information about the dynamic interaction, to a force based task specification. This eliminates the requirement of human intervention between the planning and execution phase. The presented approach applies to all compliant motions between polyhedral objects, and is verified in a real world experiment.

This paper presents a modular demonstration tool for robot programming by human demonstration and an approach for the calibration of the tool's sensors. The tool is equipped with a wrench sensor, twelve LED markers for fast and accurate six dimensional position tracking with the Krypton K600 camera system, a compact camera and a laser distance sensor. A gripper mechanism is mounted on the tool for grasping and manipulating objects. The design of the tool specifically focused on the demonstration of compliant motion task, with applications in manipulation and assembly tasks. The calibration approach first uses an extended Kalman Filter to convert the measured positions of three to twelve visible LED's into the pose of the tool frame relative to the Krypton camera frame. Then, using a non minimal state Kalman filter, the force sensor calibration parameters are calculated, and the orientation of the Krypton camera frame relative to the world frame is defined. This calibration approach is verified in a real world experiment.

Compliant motion is required or desirable in many robotic tasks, especially assembly tasks. Both planning and execution of autonomous compliant motion requires the knowledge of contact states between parts in contact beforehand. Previous research has addressed automatic generation of contact states between rigid objects. However, not all such contact states can be possibly reached if a rigid object is attached to and moved by a manipulator due to the manipulator constraints. In this paper, we study the problem of finding feasible contact states between a polyhedral part A held by a manipulator with a fixed base and a fixed polyhedral part B. Given a contact state graph between the unattached part A and the fixed part B, our approach then attaches A to the manipulator model and checks the reachability of each contact state and the connection between two neighboring contact states by applying a virtual compliant controller to the manipulator to test possible compliant motions of A. Implementation results validate the effectiveness of our method.