To improve the safety and reliability of robotic manipulators during high-speed precision movements, this paper proposes a method for smooth and time-optimal trajectory planning incorporating kinodynamic constraints. The primary objective is to use an evolutionary algorithm to determine a trajectory by considering time and jerk within the feasible path-pseudo-velocity phase plane region. Firstly, the path parameterization theory extracted the maximum pseudo-velocity projection curve from the kinodynamic constraints. Subsequently, the feasible region in the phase plane was defined through reachability analysis of discrete linear systems. Thereafter, we constructed the trajectory function using a cubic B-spline curve, optimizing its control points with an improved carnivorous plant optimization algorithm. Finally, the effectiveness and practicality of this method were verified through simulations on a 6-DOF manipulator.

Sliding electrical contacts need to be lubricated by conductive lubricants to perform low energy dissipation, high reliability, and long service life. This work studied the thermal stability, anti-corrosion capacity, and conductive, and tribological behaviors of several solid additives in multiply alkylated cyclopentanes (MACs), including carbon nanotubes (CNTs), multilayer graphene (MG), and silver microparticles. The results showed that all the additives possessed favorable thermal stability and corrosion resistance; in particular, CNTs and MG exhibited lower and more stable electrical contact resistance (ECR) and better lubricity abilities than Ag microparticles. Moreover, based on the characterization of the worn surfaces and the film thickness calculation, the favorable conductive and tribological properties of CNTs and MG were related to the high conductivity and specific structure of the additives and the good chemical inertness of MACs.

In order to meet the needs of robot’s operating tools for different types and sizes, the dexterous hand is studied by many scientific research institutions. However, the large number of joints in a dexterous hand leads to the difficulty of motion planning. Aiming at this problem, this paper proposes a planning method abased on BPNN inspired by human hands. Firstly, this paper analyses the structure and function of the human hand and summarizes its typical strategy of operation. Secondly, based on the manual operation strategy, the tools are classified according to the shape and the operation mode of the dexterous hand is presented. Thirdly, the BPNN is used to train the humanoid operation, and then output the operation plan. Finally, the simulating experiments of grasping simple tools and operating complex tools are made by MATLAB and ADAMS. The simulation verifies the effectiveness of this method.