An Efficient Stochastic Constrained Path Planner for Redundant Manipulators

This brief proposes a novel stochastic method that exploits the particular kinematics of mechanisms with redundant actuation and a well-known manipulability measure to track the desired end-effector task-space motion in an efficient manner. Whilst closed-form optimal solutions to maximise manipulability along a desired trajectory have been proposed in the literature, the solvers become unfeasible in the presence of obstacles. A manageable alternative to functional motion planning is thus proposed that exploits the inherent characteristics of null-space configurations to construct a generic solution able to improve manipulability along a task-space trajectory in the presence of obstacles. The proposed Stochastic Constrained Optimization (SCO) solution remains close to optimal whilst exhibiting computational tractability, being an attractive proposition for implementation on real robots, as shown with results in challenging simulation scenarios, as well as with a real 7R Sawyer manipulator, during surface conditioning tasks.

Combined Feedback Feedforward Control of a 3-Link Musculoskeletal System Based on the Iterative Training Method

The investigation and study of the limbs, especially the human arm, have inspired a wide range of humanoid robots, such as movement and muscle redundancy, as a human motor system. One of the main issues related to musculoskeletal systems is the joint redundancy that causes no unique answer for each angle in return for an arm’s end effector’s arbitrary trajectory. As a result, there are many architectures like the torques applied to the joints. In this study, an iterative learning controller was applied to control the 3-link musculoskeletal system’s motion with 6 muscles. In this controller, the robot’s task space was assumed as the feedforward of the controller and muscle space as the controller feedback. In both task and muscle spaces, some noises cause the system to be unstable, so a forgetting factor was used to a convergence task space output in the neighborhood of the desired trajectories. The results show that the controller performance has improved gradually by iterating the learning steps, and the error rate has decreased so that the trajectory passed by the end effector has practically matched the desired trajectory after 1000 iterations.

Research on Region Division of Large Workpiece Based on Dexterous Workspace of Robot

In order to complete the processing of large-scale workpieces, a region division of large-scale workpieces based on robot dexterous workspace is studied. The linkage coordinate system of the robot is established by D-H method, and the forward kinematics equation of the robot is obtained; Monte Carlo method is used to analyze the workspace of the robot, and MATLAB is used to program to draw the workspace and task space of the robot; Taking an example part as the object, the feature of the part is studied, and the process of determining the task space area of the workpiece in the dexterous workspace of the robot is given, and the region of the workpiece is divided based on the size of the task space and the geometric features of the workpiece.

Development of Task-Space Nonholonomic Motion Planning Algorithm Based on Lie-Algebraic Method

In this paper, a Lie-algebraic nonholonomic motion planning technique, originally designed to work in a configuration space, was extended to plan a motion within a task-space resulting from an output function considered. In both planning spaces, a generalized Campbell–Baker–Hausdorff–Dynkin formula was utilized to transform a motion planning into an inverse kinematic task known for serial manipulators. A complete, general-purpose Lie-algebraic algorithm is provided for a local motion planning of nonholonomic systems with or without output functions. Similarities and differences in motion planning within configuration and task spaces were highlighted. It appears that motion planning in a task-space can simplify a planning task and also gives an opportunity to optimize a motion of nonholonomic systems. Unfortunately, in this planning there is no way to avoid working in a configuration space. The auxiliary objective of the paper is to verify, through simulations, an impact of initial parameters on the efficiency of the planning algorithm, and to provide some hints on how to set the parameters correctly.

Systematic object-invariant in-hand manipulation via reconfigurable underactuation: Introducing the RUTH gripper

We introduce a reconfigurable underactuated robot hand able to perform systematic prehensile in-hand manipulations regardless of object size or shape. The hand utilizes a two-degree-of-freedom five-bar linkage as the palm of the gripper, with three three-phalanx underactuated fingers, jointly controlled by a single actuator, connected to the mobile revolute joints of the palm. Three actuators are used in the robot hand system in total, one for controlling the force exerted on objects by the fingers through an underactuated tendon system, and two for changing the configuration of the palm and, thus, the positioning of the fingers. This novel layout allows decoupling grasping and manipulation, facilitating the planning and execution of in-hand manipulation operations. The reconfigurable palm provides the hand with a large grasping versatility, and allows easy computation of a map between task space and joint space for manipulation based on distance-based linkage kinematics. The motion of objects of different sizes and shapes from one pose to another is then straightforward and systematic, provided the objects are kept grasped. This is guaranteed independently and passively by the underactuated fingers using a custom tendon routing method, which allows no tendon length variation when the relative finger base positions change with palm reconfigurations. We analyze the theoretical grasping workspace and grasping and manipulation capability of the hand, present algorithms for computing the manipulation map and in-hand manipulation planning, and evaluate all these experimentally. Numerical and empirical results of several manipulation trajectories with objects of different size and shape clearly demonstrate the viability of the proposed concept.

Bilateral teleoperation with object-adaptive mapping

Task space mapping approaches for bilateral teleoperation, namely object-centered ones, have yielded the most promising results. In this paper, we propose an invertible mapping approach to realize teleoperation through online motion mapping by taking into account the locations of objects or tools in manipulation skills. It is applied to bilateral teleoperation, with the goal of handling different object/tool/landmark locations in the user and robot workspaces while the remote objects are moving online. The proposed approach can generate trajectories in an online manner to adapt to moving objects, where impedance controllers allow the user to exploit the haptic feedback to teleoperate the robot. Teleoperation experiments of pick-and-place tasks and valve turning tasks are carried out with two 7-axis torque-controlled Panda robots. Our approach shows higher efficiency and adaptability compared with traditional mappings.