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This paper analyzes the problem of sensor-based colli-sion detection for an industrial robotic manipulator. A method to perform collision tests based on images taken from several stationary cameras in the work cell is pre-sented. The collision test works entirely based on the im-ages, and does not construct a representation of the Carte-sian space. It is shown how to perform a collision test for all possible robot configurations using only a single set of images taken simultaneously.
In many robotic applications, the teaching of points in space is necessary to register the robot coordinate system with the one of the application. Robot-human interaction is awkward and dangerous for the human because of the possibly large size and power of the robot, so robot movements must be predictable and natural. We present a novel hybrid control algorithm which provides the needed precision in small scale movements while allowing for fast and intuitive large scale translations.
We present a model predictive control (MPC) algorithm for online time-optimal trajectory planning of cooperative robotic manipulators. Robotic arms sharing a common confined operational space are exposed to high interrobot collision
risks. For collision avoidance, a smooth robot geometry approximation by Bézier curves is applied, utilizing velocity constraints and tangent separating planes, enabling an efficient generation of robot trajectories in real-time. The proposed optimization algorithm is validated on an experimental setup consisting of two collaborative robotic arms performing synchronous pick-and-place tasks.