The on-ground validation of orbital manipulators is a challenging task because the robot is designed for a gravity-free operational environment, but it is validated under the effect of gravity. As a consequence, joint torque limits can be easily reached in certain configurations when gravity is actively compensated by the joints. Hence, the workspace for on-ground testing is restricted. In this paper, an optimal strategy is proposed for achieving gravity compensation of an orbital manipulator arm on ground. The strategy minimizes the joint torques acting on the manipulator by solving an optimization problem and it computes the necessary forces to be tracked by an external carrier. Hence, full gravity compensation is achieved for the orbital manipulator. Experimental results validate the effectiveness of the method on the DLR CAESAR space robot, which uses a cable suspended system as external carrier to track the desired gravity compensation force, resulting from the proposed method.

The observation of robotic assembly tasks is required as feedback for decisions and adaption of the task execution on the current situation. A sequential Monte Carlo observation algorithm is proposed, which uses a fast and accurate collision detection algorithm as a reference model for the contacts between complex shaped parts. The main contribution of the paper is the extension of the classic random motion model in the propagation step with sampling methods known from the domain of probabilistic roadmap planning in order to increase the sample density in narrow passages of the configuration space. As a result, the observation performance can be improved and a risk of sample impoverishment reduced. Experimental validation is provided for a peg-in-hole task executed by a lightweight-robot arm equipped with joint torque sensors.

In this paper we present an experimental evaluation of automatic robotic assembly of complex planar parts. The torque-controlled DLR light-weight robot, equipped with an on-board camera (eye-in-hand configuration), is committed with the task of looking for given parts on a table, picking them, and inserting them inside the corresponding holes on a movable plate. Visual servoing techniques are used for fine positioning over the selected part/hole, while insertion is based on active compliance control of the robot and robust assembly planning in order to align the parts automatically with the hole. Execution of the complete task is validated through extensive experiments, and performance of humans and robot are compared in terms of overall execution time.

The paper addresses the automatic assembly of planar parts with complex geometry. Its main focus is on the automatic generation and parameterization of the assembly sequence, which should provide maximal robustness with respect to positioning errors of the robot and residual position uncertainties of vision based object localization. The assembly utilizes active or passive compliance of the robot in order to align the parts automatically. Success of the automatic alignment, i.e. the convergence of the assembly process can be guaranteed using the means of regions of attraction (ROA). The planning optimizes the assembly trajectories and parameters in such a way that the ROA is maximized for a given part geometry. For the convergence analysis, passivity properties of the robot and the environment are used. The method is validated through extensive experiments and can be successfully applied also for the automated assembly planning with passive compliance devices, as widely used today in industrial automation.