Current Issue : January - March Volume : 2021 Issue Number : 1 Articles : 5 Articles
The Internet of Things and artificial intelligence have developed rapidly in\nrecent decades, and intelligent robots have been used in various fields of\nproduction and life. There are many requirements for the functions, performance\nand intelligence of smart robots. Many researchers are committed to\nhow intelligent robots can learn more efficiently and develop new functions.\nHow to obtain the learning data while protecting the privacy of users and\nhow to record the learning process are two major problems faced by researchers.\nThis article proposes a smart robot training data acquisition and\nlearning process recording system based on blockchain to ensure that the\ntraining information of robot is kept credible and to provide users with privacy\nprotection....
Many collaborative robots use strain-wave-type transmissions due to their desirable characteristics of high torque capacity and\nlow weight. However, their inherent complex and nonlinear behavior introduces significant errors and uncertainties in the robot\ndynamics calibration, resulting in decreased performance for motion and force control tasks and lead-through programming\napplications. This paper presents a new method for calibrating the dynamic model of collaborative robots. The method combines\nthe known inverse dynamics identification model with the weighted least squares (IDIM-WLS) method for rigid robot dynamics\nwith complex nonlinear expressions for the rotor-side dynamics to obtain increased calibration accuracy by reducing the\nmodeling errors. The method relies on two angular position measurements per robot joint, one at each side of the strain-wave\ntransmission, to effectively compensate the rotor inertial torques and nonlinear dynamic friction that were identified in our\nprevious works. The calibrated dynamic model is cross-validated and its accuracy is compared to a model with parameters\nobtained from a CAD model. Relative improvements are in the range of 16.5% to 28.5% depending on the trajectory....
In this paper, we mainly solve the adaptive control problem of robot manipulators with uncertain kinematics, dynamics, and\nactuators parameters, which has been a long-standing, yet unsolved problem in the robotics field, because of the technical\ndifficulties in handling highly coupled effect between control torque and the mentioned uncertainties. To overcome the difficulties,\nwe propose a new Lyapunov-based adaptive control methodology, which effectively fuses the inverse Jacobian technique and the\nactuator adaptation law, with which the chattering in tracking errors caused by actuator parameter perturbation is well suppressed.\nIt is demonstrated that the asymptotic convergence of all closed-loop signals is guaranteed.Moreover, the effectiveness of\nour control scheme is illustrated through simulation studies....
When searching for multiple targets in an unknown complex environment, swarm robots should firstly form a number of\nsubswarms autonomously through a task division model and then each subswarm searches for a target in parallel. Based on the\nprobability response principle and multitarget division strategy, a closed-loop regulation strategy is proposed, which includes\ntarget type of member, target response intensity evaluation, and distance to the corresponding individuals. Besides, it is necessary\nto make robots avoid other robots and convex obstacles with various shapes in the unknown complex environment. By\ndecomposing the multitarget search behavior of swarm robots, a simplified virtual-force model (SVF-Model) is developed for\nindividual robots, and a control method is designed for swarm robots searching for multiple targets (SRSMT-SVF). The\nsimulation results indicate that the proposed method keeps the robot with a good performance of collision avoidance, effectively\nreducing the collision conflicts among the robots, environment, and individuals....
Robot will be used in the grinding industry widely to liberate human beings from harsh environments. In the grinding process,\noptimal trajectory planning will not only improve the processing quality but also improve the machining efficiency. The aims of\nthis study are to propose a new algorithm and verify its efficiency in achieving the optimal trajectory planning of the grinding\nrobot. An objective function has been defined terms of both time and jerk. Improved whale optimization algorithm (IWOA) is\nproposed based on whale optimization algorithm (WOA) and differential evolution algorithm (DE). Mutation operation and\nselection operation of DE are imitated in the part of initialization to process the population initialized by WOA, and then, the\nsearch tasks of WOA are performed. Motion with a constant velocity of the end-effector is considered during fine grinding. The\ncontinuity of acceleration and velocity will be achieved by minimizing jerk, and at the same time, smooth robot movement can be\nobtained. Cubic spline interpolation is implemented. A six-axis industrial robot is used for this research. Results show that optimal\ntrajectory planning based on IWOA is more efficient than others. This method presented in this paper may have some indirect\nsignificance in robot business....
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