This paper focuses on the problem of extracting the physical dynamic parameters which are fundamental for computing the positive-definite link mass matrix. To solve this problem, a minimal set of dynamic parameters were firstly identified by the standard least squares method. In order to simplify the dynamics model, a new set of essential dynamic parameters were calculated by eliminating the poorly identified parameters with an iterative approach. Based on these dynamic parameters with better identification quality, a universally global optimization framework was proposed here to retrieve the set of physical dynamic parameters of a serial robot, in which parameter bounds, linear and nonlinear constraints with physical consistency can be easily considered, such as the triangle inequality of the link inertia tensors, the total link mass limitations, other user-defined constraints and so on. Finally, validation experiments were conducted on the KUKA LBR iiwa 14 R820 robot. The results show that the proposed optimization framework is effective, and the identified dynamic parameters can predict the robot joint torques accurately for the validation trajectories.

Magnetic adsorption mechanisms are widely used for wall-climbing robots to manipulate a locomotive on the surface of a magnetic conducting metal. However, the reported magnetic adsorption mechanisms are subject to the problems such as the lack of adsorption capability, the weakness of kinematic performance, and the overwhelming detaching force. To solve the problems, a novel style of a permanent-magnetic adsorption mechanism using an electromagnetic method and internal force compensation principle is detailed in this work. Specifically, a permanent magnet, an electromagnet, and a nonlinear spring are configurated to achieve a reliable adsorption function by using the minimal detaching force. Following that, the results obtained from both the finite element analysis and the experiments carried out by using a prototype demonstrated its effectiveness. It does not only have a rapid and controllable adsorption-detachment capacity in reference to the magnetic conducting surface but also has low power consumption, large adsorption force, and reliable and safe performance.

Frogs are vertebrate amphibians with both efficient swimming and jumping abilities due to their well-developed hind legs. They can jump over obstacles that are many or even tens of times their size on land. However, most of the current jumping mechanisms of biomimetic robotic frogs use simple four-bar linkage mechanisms, which has an unsatisfactory biomimetic effect on the appearance and movement characteristics of frogs. At the same time, multi-joint jumping robots with biomimetic characteristics are subject to high drive power requirements for jumping action. In this paper, a novel jumping mechanism of a biomimetic robotic frog is proposed. Firstly, the structural design of the forelimb and hindlimb of the frog is given, and the hindlimb of the robotic frog is optimized based on the design of a single-degree-of-freedom six-bar linkage. A simplified model is established to simulate the jumping motion. Secondly, a spring energy storage and trigger mechanism is designed, including incomplete gear, one-way bearing, torsion spring, and so on, to realize the complete jumping function of the robot, that is, elastic energy storage and regulation, elastic energy release, and rapid leg retraction. Thirdly, the experimental prototype of the biomimetic robotic frog is fabricated. Finally, the rationality and feasibility of the jumping mechanism are verified by a jumping experiment. This work provides a technical and theoretical basis for the design and development of a high-performance amphibious biomimetic robotic frog.

The inertial parameters of payloads attached to the end effector of robots benefit to several robotics applications, such as the model-based control, the task optimization, and so on. These applications require the accurate estimation of the inertial parameters. In the existing payload estimation approaches, however, the data weighting technique, which can reduce the adverse effects of outliers and significantly improve the final results, has not been applied yet. In this article, an accurate identification method based on double weighting for inertial parameters of robot payloads is proposed. In order to obtain the weighting matrices, a modified dynamic parameter identification method with two loops is firstly proposed. Then, based on the identified results of dynamic parameters, a payload identification model based on double weighting is constructed. In addition, the variations of both nonlinear friction parameters and linear friction parameters caused by the payload are considered in this model. Finally, experimental comparisons between our method and another four methods are conducted. The results confirm that our method shows the best performance, especially on improving the identification accuracy of mass and center of mass of the payload.