SIMULATING THE TIME-VARYING PARAMETERS OF ROBOTS IN PERFORMING THE COMPLEX SEQUENTIAL TASKS

Abstract

To improve the performance in complex sequential chores, simulating time-varying parameters in robotic systems is essential. This review paper explores an advanced computational framework for modeling and analyzing the dynamic parameters of robots like position, torque, velocity, and force during sequential operations. Time-varying factors play a key role in defining the accuracy and efficiency of robotic tasks, specifically in environments where tasks are multi-stage, subject to changing conditions, and non-repetitive. The recommended simulation model incorporates important techniques including kinematic modeling, adaptive control algorithms, and nonlinear dynamic equations of motion to deliver real-time apprises on robot performance under joint friction, external disturbances, and varying loads. A multi-parameter time-series approach is utilized to simulate the unceasing interaction between robotic systems and their working environments. The model put on finite element analysis to simulate machine-driven deformation, and stress confirming the consistency of the robot’s structure during task execution. The review also includes reinforcement learning to let robots self-improve in real-time as tasks progress, adjusting to unexpected variables like terrain changes, task priorities, and fluctuating payloads. The dynamic task scheduling is controlled by Markov decision processes which allow well-organized switching between tasks whilst minimizing resource consumption and downtime. An inverse dynamics approach is engaged to compute actuator forces and joint torques essential for the execution of the wanted movements, allowing for real-time adjustments in speed and trajectory. To enhance the simulation's fidelity, sensor fusion procedures are applied, joining data from multiple/compound sensors e.g., gyroscopes, force/torque sensors, and cameras, etc. to deal with widespread feedback on the robot’s interface with its environment. This feedback is managed by Kalman filters to alleviate noise and offer correct apprises to the control system. Experiments results conducted on mobile robots and industrial robots performing tasks such as object manipulation, navigation, and assembly line operations through dynamic environments demonstrate that the simulated robots can adjust to time-varying factors with greater accuracy and less error margins, helping improved operational robustness and task efficiency. The usage of trajectory optimization algorithms in the simulation has shown a noteworthy decrease in wear on robot joints and energy consumption by smoothing out motion paths and preventing sudden changes in movement. Therefore, this review focuses on the presentation of a healthy simulation framework that efficiently adapts and models to the time-varying parameters of robots during complex sequential tasks.