Articulated robot

About: Articulated robot is a research topic. Over the lifetime, 4364 publications have been published within this topic receiving 52442 citations.

Trajectory planning algorithm based on the continuity of jerk

Abstract: This paper presents a trajectory planning method that provides a continuity of position, velocity, acceleration and jerk. The method combines fifth-order and fourth-order polynomials in order to satisfy the continuity of jerk and give smooth accelerations on all segments of the planned trajectory. The proposed method was tested for movements of the three-axes planar articulated robot. The method includes limits of the velocities, accelerations, and jerks of each robot joint. Simulation results are presented and compared with the results obtained by the original Ho and Cook spline-interpolation method.

Real time human motion imitation of anthropomorphic dual arm robot based on Cartesian impedance control

Abstract: This paper presented a real-time human motion imitation approach to control an anthropomorphic dual arm robot by human demonstration. We use the processed positions of human skeleton joints from Kinect sensor as commands directly to control the robot arms by using Cartesian impedance control to follow the human motion without solving inverse kinematics problem. In order to avoid a jerky robot arm motion, we apply an on-line trajectory generator algorithm to obtain a smooth movement trajectory by imposing the limit of velocity and acceleration. Moreover, the self-collision problem has also been considered. When the distance between two parts of body is close enough, a repulsive force will automatically generate to prevent collision. Taking the robot capability and safe issue into account, the output force is restricted to ensure that the action of robot is stable. We demonstrate the feasibility of the approach by implementing the human motion imitation system on a humanoid dual arm robot developed in our lab. The experimental results show that the system is in good practice and flexible enough to imitate various human motions.

Shared Control Design of a Walking-Assistant Robot

Abstract: This brief presents a control design for a walking-assistant robot in a complex indoor environment, such that it can assist a walking-impaired person to walk and avoid unexpected obstacles. In this design, the robot motion is a resultant of autonomous navigation and compliant motion control. The compliance motion controller allows the robot to possess passive behavior following the motion intent of the user, while the autonomous guidance gives safe navigation of the robot without colliding with any obstacles. A shared-control approach is suggested to combine the passive compliant behavior and safe guidance of the robot. When a user exerts force to the robot, the mobile platform responds to adjust the speed in compliance with the user movement. On the other hand, the autonomous navigation controller is designed to provide collision-free guidance. Using the developed shared controller, outputs of the compliance motion controller and autonomous navigation controller are fused to generate appropriate motion for the robot. In this manner, passive behavior allows the walking-assistant robot to adapt to a user’s motion intent and move in compliance with user. Meanwhile, the active guidance adjusts the linear velocity and the direction of the robot in real time in response to the environmental data received from the on-board laser scanner. The developed algorithms have been implemented on a self-constructed walking-assistant robot. Experimental results validate the proposed design and demonstrate that the robot can actively avoid unexpected obstacles while move passively following the user to the destination.

Articulated Robot Motion for Simultaneous Localization and Mapping (ARM-SLAM)

Abstract: A robot with a hand-mounted depth sensor scans a scene. When the robot's joint angles are not known with certainty, how can it best reconstruct the scene? In this work, we simultaneously estimate the joint angles of the robot and reconstruct a dense volumetric model of the scene. In this way, we perform simultaneous localization and mapping in the configuration space of the robot, rather than in the pose space of the camera. We show using simulations and robot experiments that our approach greatly reduces both 3D reconstruction error and joint angle error over simply using the forward kinematics. Unlike other approaches, ours directly reasons about robot joint angles, and can use these to constrain the pose of the sensor. Because of this, it is more robust to missing or ambiguous depth data than approaches that are unconstrained by the robot's kinematics.