تحسين تعقب المسار لنظام روبوت متنقل ذاتي الحركة

Optimization of Path Tracking of Self-Acting Mobile Robotic System

Abstract

An autonomous mobile robot system such as an autonomous vehicle requires high criteria of accuracy to track the desired speed profiles and predefined trajectory. Some challenges in the development of self-acting systems, like building a mathematical model, designing longitudinal and lateral control systems, and the optimization technique are still significant topics.
This thesis is concerned with constructing the (vehicle and bicycle) models which govern the motion of the autonomous vehicle in terms of the longitudinal dynamic vehicle model and both longitudinal, lateral motions for the kinematic bicycle model. To set the motion of the driverless vehicle, various control strategies are proposed.
The Proportional Integral Derivative (PID) controller was suggested to manipulate throttle/brake actuators of the longitudinal dynamic vehicle model to track various reference speeds. Besides, the proportional (P) controller was proposed for longitudinal motion while four controllers for lateral motion were proposed to steer the kinematic bicycle model at various speeds correctly which are: Path Tracking Modified Proportional Integral Derivative (PTMPID), PID, Stanley, and Modified Stanley (MS) controllers. The comparison between lateral controllers was accomplished to know which one is the best.
The parameters of the controllers are optimized with two new optimization methods: Hybrid Salp Swarm and Butterfly Optimization Algorithms (HSSABOA1 and HSSABOA2). However, to investigate the performance competence of the suggested algorithms, they have been compared with the basic algorithms: Butterfly Optimization Algorithm (BOA) and Salp Swarm Algorithm (SSA) in addition to commonly used algorithms; Genetic Algorithm (GA), Ant Colony Optimization (ACO) algorithm, and Particle Swarm Optimization (PSO) algorithm. In these optimization techniques, the Integral Absolute Error (IAE) and Root Mean Square Error (RMSE) are used as objective functions to minimize tracing errors of the speed and steering, respectively.
The simulation results found that the PID controller based on HSSABOA1 for the dynamic vehicle model has the best solution to reduce the speed error with an improvement percentage of (8.6088%), (5.0226%), (2.5074%), (0.0306%), and (0.2295%) than PID based on GA, ACO, PSO, BOA, and SSA, in the order. Moreover, it also has traced various speed profiles successfully.
In contrast, the PTMPID based on HSSABOA1 has better performance; in the minimization of the lateral error beside a percentage enhancement by (10.523%), (19.456%), (83.276%), (18.263%), and (94.005%), than PTMPID-SSA, PTMPID-BOA, Stanley-HSSABOA2, modified Stanley-HSSABOA2, and PID-HSSABOA1, respectively. In addition, it also managed to track the road maneuver with various longitudinal speeds without large vacillation.