Robots frequently need to work in human environments and handle many different types of objects. There are two problems that make this challenging for robots: human environments are typically cluttered, and the multi-finger robot hand needs to grasp and to lift objects without knowing their mass and damping properties. Therefore, this study combined vision and robot hand real-time grasp control action to achieve reliable and accurate object grasping in a cluttered scene. An efficient online algorithm for collision-free grasping pose generation according to a bounding box is proposed, and the grasp pose will be further checked for grasp quality. Finally, by fusing all available sensor data appropriately, an intelligent real-time grasp system was achieved that is reliable enough to handle various objects with unknown weights, friction, and stiffness. The robots used in this paper are the NTU 21-DOF five-finger robot hand and the NTU 6-DOF robot arm, which are both constructed by our Lab.

This paper presents a new solution for inverse kinematics (IK) by using dual quaternions (DQ) as operators and combining them with the Paden–Kahan subproblem to determine the analytical solution. Kinematics under the structure of screw theory makes the process mathematically efficient and gives geometric meaning. Both derived results and simulation outgrowths, when compared with the generally used numerical solution and matrix-based analytical solution, show that our method is faster and does not suffer from the numerical instability caused by being near to a singular configuration. The method has been proven to have the advantages of reducing the computational load and promoting precision, and it can provide multiple choices for IK.

This paper describes how a standard HI-tech WINner (HIWIN) Corporation articulated six degrees of freedom (DOFs) industrial robot arm and a National Taiwan University five-finger robotic hand were equipped with additional hardware and software to enable the resulting machine to play chess with a human chess player. For automatic interaction with the human player, moves were recorded by a webcam and automatically analyzed. No manual (keyboard) input was necessary. The chess-playing robotic system has been provided. An innovative humanoid robotic hand with 12 DOFs and 19 joints were designed, and distributed tactile sensor arrays with 376 detecting points on its surface were developed. The hand can communicate with the external through controller area networks bus. For performance evaluation of the designed robotic hand, we analyzed the workspace, intersection volume, and manipulability. First, several anatomical analyses were conducted prior to a decision on the kinematic design. Optimization procedures were then developed in this study to improve the parameters of the design and structure of the mechanism. The concepts of a series elastic actuator (SEA) and an under-actuated mechanism were also employed to give the robotic hand a compliant property and high dexterity; hence, a humanoid robotic hand was devised. Since the robotic hand is as small and dexterous as a human hand, it can be conveniently used for a wide range of applications. Finally, the system has been proved to be executable in a complex environment.

Many recent approaches have successfully generated a stable walking pattern for biped robots, but discussions about its optimization are relatively few. In this paper, a Center of Gravity (COG) trajectory optimization method is proposed to minimize the cost function of joint torque, joint limit, and joint speed limit. The linear quadratic control-based inverted pendulum controller optimizes the COG trajectories in sagittal and lateral directions with the COG height trajectory. The COG height trajectory is optimized by finding the derivative of the cost function with respect to the COG height offline. Then the proposed walking pattern generator builds the COG height trajectory database of different walking steps for online connection of a walking pattern. The walking pattern generator is verified by experiments and simulations of different step cycles with our humanoid robot, NINO, and it can clearly reduce the required joint torque of the robot while walking. In addition, compared with the fixed COG height trajectory, the energy consumption is reduced by 14% from the experimental results. Thus, the method succeeds in generating a more energy-saving walking pattern.