A safe and energy efficient robotic system for industrial automatic tests on domestic appliances: Problem statement and proof of concept

In this paper, the design and the development of a robotic platform conceived to perform accelerated life tests on a newly manufactured domestic appliances is presented. The proposed system aims at improving the safety of human operators that share the workspace with the robotic platform which is a common scenario of test laboratories. A deep learning algorithm is used for the human detection and pose estimation, while the integration between a conventional motion planning algorithm with a fast 3D collision checker has been implemented as a global planner plugin for the ROS navigation stack. With the twofold objective of improving safety and saving energy in the battery-powered mobile manipulator used in this project, the problem of minimizing the overall kinetic energy is addressed through a properly designed task priority controller, in which the manipulator inertia matrix is used to weight the joint speeds while satisfying multiple robotic tasks according to a hierarchy designed to interact with the appliances while preserving the safety of the human operators. Simulations are carried out to evaluate the overall control architecture and preliminary results indicate the effectiveness of the developed system in the test laboratory floors

A Motion Planner For Robot Manipulators Based on Support Vector Machines

ABSTRACT Moving a robot between two configurations without making a collision is of high importance in planning problems. Sampling-based planners have gained popularity due to their acceptable performance in practical situations. This body of work introduces the notion of a risk function that is provided using the Support Vector Machine (SVM) algorithm to find safe configurations in a sampled configuration space. A configuration is called safe if it is placed at maximum dis­tance from surrounding obstacle samples. Compared to previous solutions, this function is less sensitive to a selected sampling method and resolution. The proposed function is first used as a repulsive potential field in a local SVM-based planner. Afterwards, a global planner using the notion of the risk function is suggested to address some of the shortcomings of the suggested local planner. The proposed global planner is able to solve a problem with fewer number of milestones and less number of referrals to the collision detection module in comparison to the classical Probabilistic Roadmap Planner (PRM). The two proposed methods are evaluated in both simulated and experimental environments and the results are reported