Generating a collision-free robot motion is crucial for safe applications in real-world settings. This requires an accurate model of all obstacle shapes within the constrained robot cell, which is particularly challenging and time-consuming. The difficulty is heightened in flexible production lines, where the environment model must be updated each time the robot cell is modified. Furthermore, sensor-based methods often necessitate costly hardware and calibration procedures, and can be influenced by environmental factors (e.g., light conditions or reflections). To address these challenges, we present a novel data-driven approach to modeling a cluttered workspace, leveraging solely the robot internal joint encoders to capture exploratory motions. By computing the corresponding swept volume, we generate a (conservative) mesh of the environment that is subsequently used for collision checking within established path planning and control methods. Our method significantly reduces the complexity and cost of classical environment modeling by removing the need for CAD files and external sensors. We validate the approach with the KUKA LBR iisy collaborative robot in a pick-and-place scenario. In less than three minutes of exploratory robot motions and less than four additional minutes of computation time, we obtain an accurate model that enables collision-free motions. Our approach is intuitive, easy-to-use, making it accessible to users without specialized technical knowledge. It is applicable to all types of industrial robots or cobots.

Our main goal is to show how a robotic cell can withstand In most factories today the robotic cells are deployed on external forces occurring on the move. To achieve well enforced bases to avoid any external impact on the this goal, we take the Agile Robotics for Industrial accuracy of production. In contrast to that, we evaluate Automation Competition (ARIAC) 2018 environment a futuristic concept where the whole robotic cell (ariac2018 2018) as a baseline, and extend it to serve could work in a moving platform. Imagine a trailer of our needs. First, we modified the static environment a truck moving along the motorway while exposed to and mobilized it. The next step was to apply external heavy physical impacts due to maneuvering. The key forces from different sources to the modified model. Our question here is how the robotic cell behaves and how final goal is to examine the productivity changes in the the productivity is affected. We propose a system architecture moving system, and based on the results, propose suggestions (FATHER) and show some solutions including to decrease the impact of the external forces.

Benchmarking provides experimental evidence of the scientific baseline to enhance the progression of fundamental research, which is also applicable to robotics. In this paper, we propose a method to benchmark metrics of robotic manipulation, which addresses the spatial-temporal reasoning skills for robot learning with the jigsaw game. In particular, our approach exploits a simple set of jigsaw pieces by designing a structured protocol, which can be highly customizable according to a wide range of task specifications. Researchers can selectively adopt the proposed protocol to benchmark their research outputs, on a comparable scale in the functional, task, and system-level of details. The purpose is to provide a potential look-up table for learning-based robot manipulation, commonly available in other engineering disciplines, to facilitate the adoption of robotics through calculated, empirical, and systematic experimental evidence.

Daniela Rus is a robot evangelist. She challenged a packed audience in the Interdisciplinary Science and Engineering Complex on Tuesday to imagine a world where robots free us to be more creative by taking care of all our physical tasks--from playing with our pets to performing surgery without an incision. As director of the Massachusetts Institute of Technology's Computer Science and Artificial Intelligence Laboratory, Rus delivered the inaugural lecture in Northeastern's Distinguished Speaker Series in Robots. "Imagine a world where you're being driven home by your autonomous car," said Rus. "Your car is connected to your refrigerator, which tells it what ingredients you need for dinner. The car is also connected to the grocery store, which is run by robots that fill your bags so they are ready when you drive up. Then you bring the food home to the robot cook and you happily let your children help in the kitchen even though they make a mess, because the mess will be taken care of by the cleaning robot."