Dynamic Simulation Modeling of Industrial Robot Kinematics in Industry 4.0

This paper studies the kinematic dynamic simulation modeling of industrial robots in the Industry 4.0 environment and guides the kinematic dynamic simulation modeling of industrial robots in the Industry 4.0 environment in the context of the research. To address the problem that each parameter error has different degrees of influence on the end position error, a method is proposed to calculate the influence weight of each parameter error on the end position error based on the MD-H error model. The error model is established based on the MD-H method and the principle of differential transformation, and then the function of uniform variation of six joint angles with time t is constructed to ensure that each linkage geometric parameter is involved in the motion causing error accumulation. Through the analysis of the robot marking process, the inverse solution is optimized for multiple solutions, and a unique engineering solution is obtained. Linear interpolation, parabolic interpolation, polynomial interpolation, and spline curve interpolation are performed on the results after multisolution optimization in the joint angle, and the pros and cons of various interpolation results are analyzed. The trajectory planning and simulation of industrial robots in the Industry 4.0 environment are carried out by using a special toolbox. The advantages and disadvantages of the two planning methods are compared, and the joint space trajectory planning method is selected to study the planning of its third and fifth polynomials. The kinetic characteristics of the robot were simulated and tested by experimental methods, and the reliability of the simulation results of the kinetic characteristics was verified. The kinematic solutions of industrial robots and the results of multisolution optimization are simulated. The methods, theories, and strategies studied in this paper are slightly modified to provide theoretical and practical support for another dynamic simulation modeling of industrial robot kinematics with various geometries.

Reinforcement Learning Path Planning Method with Error Estimation

Path planning is often considered as an important task in autonomous driving applications. Current planning method only concerns the knowledge of robot kinematics, however, in GPS denied environments, the robot odometry sensor often causes accumulated error. To address this problem, an improved path planning algorithm is proposed based on reinforcement learning method, which also calculates the characteristics of the cumulated error during the planning procedure. The cumulative error path is calculated by the map with convex target processing, while modifying the algorithm reward and punishment parameters based on the error estimation strategy. To verify the proposed approach, simulation experiments exhibited that the algorithm effectively avoid the error drift in path planning.

Multi-Target Coordinated Search Algorithm for Swarm Robotics Considering Practical Constraints

In order to deal with the multi-target search problems for swarm robots in unknown complex environments, a multi-target coordinated search algorithm for swarm robots considering practical constraints is proposed in this paper. Firstly, according to the target detection situation of swarm robots, an ideal search algorithm framework combining the strategy of roaming search and coordinated search is established. Secondly, based on the framework of the multi-target search algorithm, a simplified virtual force model is combined, which effectively overcomes the real-time obstacle avoidance problem in the target search of swarm robots. Finally, in order to solve the distributed communication problem in the multi-target search of swarm robots, a distributed neighborhood communication mechanism based on a time-varying characteristic swarm with a restricted random line of sight is proposed, and which is combined with the multi-target search framework. For the swarm robot kinematics, obstacle avoidance, and communication constraints of swarm robots, the proposed multi-target search strategy is more stable, efficient, and practical than the previous methods. The effectiveness of this proposed method is verified by numerical simulations.

A Review of Mobile Robot Navigation System for Volcano Monitoring Application

Volcano is a geological environment including magma, eruption, volcanic edifice and its basements. For continuous monitoring after eruption, a mobile robot could be proposed as an alternative to prevent hazardous effect to volcanologist who perform up close monitoring. In this paper, the robots were divided into 3 types according to their different structures: legged, track-legged and wheeled mobile robots. Meanwhile, the navigation system were implemented in 4 steps suitable for volcano condition: environment mapping, trajectory design, motion control and obstacle avoidance. These navigation system also tested in different locations: indoor, outdoor and real volcano with different testing method for these robots. The testing result was discussed in robot kinematics parameter such as trajectory, velocity, slope angle, rollover and sideslip angels.

A Review of Mobile Robot Navigation System for Volcano Monitoring Application

Volcano is a geological environment including magma, eruption, volcanic edifice and its basements. For continuous monitoring after eruption, a mobile robot could be proposed as an alternative to prevent hazardous effect to volcanologist who perform up close monitoring. In this paper, the robots were divided into 3 types according to their different structures: legged, track-legged and wheeled mobile robots. Meanwhile, the navigation system were implemented in 4 steps suitable for volcano condition: environment mapping, trajectory design, motion control and obstacle avoidance. These navigation system also tested in different locations: indoor, outdoor and real volcano with different testing method for these robots. The testing result was discussed in robot kinematics parameter such as trajectory, velocity, slope angle, rollover and sideslip angels.

Inverse Kinematic Control of a Delta Robot Using Neural Networks in Real-Time

This paper presents an inverse kinematic controller using neural networks for trajectory controlling of a delta robot in real-time. The developed control scheme is purely data-driven and does not require prior knowledge of the delta robot kinematics. Moreover, it can adapt to the changes in the kinematics of the robot. For developing the controller, the kinematic model of the delta robot is estimated by using neural networks. Then, the trained neural networks are configured as a controller in the system. The parameters of the neural networks are updated while the robot follows a path to adaptively compensate for modeling uncertainties and external disturbances of the control system. One of the main contributions of this paper is to show that updating the parameters of neural networks offers a smaller tracking error in inverse kinematic control of a delta robot with consideration of joint backlash. Different simulations and experiments are conducted to verify the proposed controller. The results show that in the presence of external disturbance, the error in trajectory tracking is bounded, and the negative effect of joint backlash in trajectory tracking is reduced. The developed method provides a new approach to the inverse kinematic control of a delta robot.