Titlebook: Interactive Collaborative Robotics; 7th International Co Andrey Ronzhin,Roman Meshcheryakov,Zhen Xiantong Conference proceedings 2022 The E

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LIRS-ArtBul: Design, Modelling and Construction of an Omnidirectional Chassis for a Modular Multipubot Operating System (ROS). For control Raspberry PI 4 and Arduino Mega2560 modules were employed. The chassis model behavior was validated in virtual environments of Gazebo and, after assembling, in real world locomotion scenarios. The chassis will be further employed in development of several new

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anachronistic

Evaluation of RGB-D SLAM in Large Indoor Environments,rridors and halls, while varying the odometer noise for a more realistic setup. We provide both qualitative and quantitative analysis of both methods uncovering their strengths and weaknesses. We find that both methods build a high-quality map with low odometry noise but tend to fail with high odome

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Goal and Force Switching Policy for DMP-Based Manipulation, goal is defined by the simplified predictive control model. We evaluate our approach on the tasks of reaching target point by end effector and pushing elevator button with UR5 collaborative manipulator. Evaluation is made in Isaac and URSim simulation taking in mind dynamics and functionality of th

esoteric

Impedance Control of an Elastic Actuator with Strongly Coupled Structure,tic torques as well. Hence, the control system should provide as much decoupling as possible to get satisfactory performance. A mathematical model of this joint has been designed as well as a control system based on elastic structure preserving impedance control. The latter has been verified by simu

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Door Opening Strategy for Mobile Manipulator with Constrained Configuration,. The trajectory of the platform is defined by the model predictive planner in such a way that it avoids pushing the arm into a singular configuration, while the manipulator is operated via high-level impedance control. In our experiments, a mobile manipulator composed from the wheeled platform and

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Domain Randomization with Adaptive Weight Distillation, single policy and the potential imperfect teacher of a multiple-policy ensemble. We compare DRAWD with two baselines on two Mujoco continuous control tasks. Our results show that the target domain performance of policies trained with DRAWD is better than the other two baselines on the metric of var

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