Dynamics modeling and path following controller of tractor-trailer-wheeled robots considering wheels slip.

To improve the payload capacity and maneuverability of a Differentially-Driven Wheeled Robot (DDWR), a wheeled vehicle, which is called trailer, is connected to the DDWR. In all of the previous studies of DDWRs with a trailer, the robot wheels are subject to pure rolling constraints. However, when these multibody systems move with high velocities/accelerations, transfer a heavy payload, or travel on a slippery surface, they experience slipping and/or skidding. In the present study, a Tractor-Trailer Wheeled Mobile Robot (TTWMR) is modeled whose wheels may slip in lateral and longitudinal directions. To this end, Lagrange formalism is employed along with the LuGre friction model to derive dynamics of the considered multibody system. Next, the problem of path following for the trailer is addressed. Toward this goal, the partial feedback linearization technique will be utilized. The obtained simulation results prove the superiority of the performance of the suggested method in comparison to the previous studies. Additionally, the response of the system in the presence of the external disturbances and uncertainties in system parameters will be examined.

Dataset for classifying and estimating the position, orientation, and dimensions of a list of primitive objects.

OBJECTIVES: Robotic systems are moving toward more interaction with the environment, which requires improving environmental perception methods. The concept of primitive objects simplified the perception of the environment and is frequently used in various fields of robotics, significantly in the grasping challenge. After reviewing the related resources and datasets, we could not find a suitable dataset for our purpose, so we decided to create a dataset to train deep neural networks to classify a primitive object and estimate its position, orientation, and dimensions described in this report. DATA DESCRIPTION: This dataset contains 8000 virtual data for four primitive objects, including sphere, cylinder, cube, and rectangular sheet with dimensions between 10 to 150 mm, and 200 real data of these four types of objects. Real data are provided by Intel Realsense SR300 3D camera, and virtual data are generated using the Gazebo simulator. Raw data are generated in.pcd format in both virtual and real types. Data labels include values of the object type and its position, orientation, and dimensions.

Design and control of a lower limb rehabilitation robot considering undesirable torques of the patient's limb.

This research introduces a new exoskeleton-type rehabilitation robot, which can be used in lower limb rehabilitation therapy for post-stroke patients. A novel design of a typical knee and ankle rehabilitation robot is proposed. The kinematic and dynamic models of the knee and ankle rehabilitation robot are derived. Furthermore, a super-twisting nonsingular terminal sliding mode control is developed to achieve the desired training missions and its results are compared with those of an adaptive sliding mode control. To reduce undesired interaction torques between knee and ankle rehabilitation robot and patient, an admittance control algorithm is added to the controller to guarantee a safe therapy session. The admittance super-twisting nonsingular terminal sliding mode control structure is considered as the novelty of this article. Taking into account the dynamic uncertainties, external disturbances, and the interaction torques, the validity of the admittance super-twisting nonsingular terminal sliding mode control controller is approved by various numerical simulations over the admittance adaptive sliding mode control.