The balance goal of the existing offline dynamic balance detection method is to minimize the amplitude of each vibration measurement point. After balancing, many high-speed rotors need to be equipped with an online dynamic balance system to maintain the rotor at a higher speed and run smoothly. This shows the existing offline dynamic that has much room for improvement in balance. A detection method that can measure the eccentric mass at each position of the rotor axis is proposed. Now the goal is to fit the curve of the center of inertia closer to the geometric center, hoping to minimize the eccentric mass on the rotor.
This paper presents a perspective on some issues related to safety in the context of autonomous surgical robots. To meet the challenge of safety certification and bring about acceptance of the technology by the public, we propose principles for a design paradigm that goes in the direction of safety by construction: design with certification in mind, clearly distinguish the notion of safety from that of responsibility, view the human component as scaffolding in the progressive transfer of decision-making to the machine, preserve interpretability by renouncing black-box approaches, leverage interpretability to assign responsibility, and take corrective action only when the semantic of the human-machine interface is violated.
The permanent magnetic suspension dust-free conveying system is introduced. According to the suspension force model and the prototype structure, a nonlinear model of the control decoupling system is established. Using the coordinate transformation decoupling strategy, four-point independent control is changed to a three-degree-of-freedom control suspension vehicle. Finally, PID control and fuzzy PID control are used to simulate the displacement step of the permanent magnet levitation vehicle with three degrees of freedom. The simulation results show that the PID controller has a faster response speed and can return to a stable position after several oscillations. The adjustment time is 0.3 seconds, but the overshoot is relatively large, about 8%. For the fuzzy PID platform, the rise time is about 0.2s, the steady-state error is about 1%, there is no overshoot, and the system has good rigidity, damping characteristics, and robustness.
This work has been carried out to implement the Rapidly exploring Random Tree (RRT) Algorithm for designing a path in a 2D workspace cluttered with different human activities perceived as obstacles of different densities in a collaborative human-robot search operation-based Industry 4.0 environment. RRT based learning is an autonomous path planning algorithm suitable for perceiving environments with differential and non-holonomic constraints. Paths have been generated between an initial point, START to destination point, GOAL amidst uncertain temporal objects met when they are near and far. The algorithm starts to expand the search tree from the START node by randomly exploring the workspace until the GOAL is reached in the presence of these obstacles. The solution is a locus of points in the workspace between source and destination. This has been done for sparsely distributed human activity as obstacles encapsulating waypoint and lower and higher obstacle densities as observed in a real collaborative search environment. The generated paths were evaluated for closeness and impact of different kinds of dynamic obstacle configurations in a real experimental test bed.
Assisted drainage robot has the function of helping users to complete toilet requirements. However, without knowing the sitting posture and height of the user, it is difficult for the robot to find a suitable position for the user. Some scholars have studied the reinforcement learning control robot according to the sitting posture model to find the user's sitting position, but the moving speed and the process of finding the sitting position are slow. In order to overcome the shortcomings of the previous research, this paper proposes a control method based on the combination of Q learning and evaluation reward. First, the robot obtains the distance between human and robot as the motion state. Then, the best exercise strategy is calculated through Q learning, and the best exercise strategy is moved to the appropriate sitting position of the user. Finally, the seat position is roughly determined through reward evaluation. Experimental simulation shows that the method of finding the user's sitting point based on Q learning control method is better than the reinforcement learning control method based on sitting posture model.
