Intelligent Adaptive Motion Control for Ground Wheeled Vehicles

In this paper a new intelligent adaptive control is applied to solve a problem of motion control of ground vehicles with two independent wheels actuated by a differential drive. The major objective of this work is to obtain a motion control system by using a new fuzzy inference mechanism where the Lyapunov’s stability can be assured. In particular the parameters of the kinematical control law are obtained using an intelligent Fuzzy mechanism, where the properties of the Fuzzy maps have been established to have the stability above. Due to the nonlinear map of the intelligent fuzzy inference mechanism (i.e. fuzzy rules and value of the rule), the parameters above are not constant, but, time after time, based on empirical fuzzy rules, they are updated in function of the values of the tracking errors. Since the fuzzy maps are adjusted based on the control performances, the parameters updating assures a robustness and fast convergence of the tracking errors. Also, since the vehicle dynamics and kinematics can be completely unknown, a dynamical and kinematical adaptive control is added. The proposed fuzzy controller has been implemented for a real nonholonomic electrical vehicle. Therefore system robustness and stability performance are verified through simulations and experimental studies

Neuro-fuzzy controller in real-time feedback schedulers

Traditional scheduling algorithms worked on closed and highly predictable environments. However present day systems need to work in more open and unpredictable environments; such as mobile robots, on-line trading, e-commerce, multimedia that cannot be driven well with traditional open-loop algorithms. A new scheduling paradigm, feedback control scheduling, therefore has been presented recently to fulfil the requirements of such systems. This algorithm defines error terms for schedules, monitors the error, and continuously adjusts the schedule to maintain stable performance. When PID (Proportional-Integral-Derivative) controller is used to control the CPU utilization, one of the problems faced is that when utilization setpoint is closer to 100%, in severely overloaded conditions, systems can have a longer settling time than the analysis based on the linear model since utilization feedback saturates at 100%. To overcome this problem, a neuro-fuzzy controller is designed instead of PID. Simulations showed that settling time with the neuro-fuzzy controller is approximately four times shorter than the one with the PID controller

A Systematic Literature Review of Path-Planning Strategies for Robot Navigation in Unknown Environment

The Many industries, including ports, space, surveillance, military, medicine and agriculture have benefited greatly from mobile robot technology.  An autonomous mobile robot navigates in situations that are both static and dynamic. As a result, robotics experts have proposed a range of strategies. Perception, localization, path planning, and motion control are all required for mobile robot navigation. However, Path planning is a critical component of a quick and secure navigation. Over the previous few decades, many path-planning algorithms have been developed. Despite the fact that the majority of mobile robot applications take place in static environments, there is a scarcity of algorithms capable of guiding robots in dynamic contexts. This review compares qualitatively mobile robot path-planning systems capable of navigating robots in static and dynamic situations. Artificial potential fields, fuzzy logic, genetic algorithms, neural networks, particle swarm optimization, artificial bee colonies, bacterial foraging optimization, and ant-colony are all discussed in the paper. Each method's application domain, navigation technique and validation context are discussed and commonly utilized cutting-edge methods are analyzed. This research will help researchers choose appropriate path-planning approaches for various applications including robotic cranes at the sea ports as well as discover gaps for optimization

Prototyping and Integration of Educational Low-Cost Mobile Robot Platform

This paper describes the process of designing and prototyping a low-cost robotic platform based on existing equipment and projects that enable extracurricular STEM activities in Croatia and beyond. A robotic platform with a differential drive configuration was chosen for education from an early age due to its simplicity and a wide range of cheap and compatible components from which it can be made. From the aspect of integration into extracurricular or curricular activities, the BBC micro:bit ecosystem was considered, enabling block-based visual programming. Components with printable parts make up the assembly of the educational robot. The main steps in designing and creating a robot prototype are presented, which consist of the modelling, 3D printing of robot parts, and assembly into a functional system. After several stages of testing, an interactive workshop was held with 7th-grade primary school pupils. Further work is planned to create educational material for extracurricular STEM workshops

E-puck motion control using multi-objective particle swarm optimization

This article describes the velocity-based motion and orientation control method for a differential-driven two-wheeled E-puck Robot (DDER) using the Multi-Objective Particle Swarm Optimization (MPSO) algorithm in the Virtual Robot Experimentation Platform (V-REP) software environment. The wheel velocities data and Infra-Red (IR) sensors reading make the multi-objective fitness functions for MPSO. We use front, left, and right IR sensors reading and right wheel velocity data to design the first fitness function for MPSO. Similarly, the front, left, and right IR sensors reading, and left wheel velocity data have been taken for making the second fitness function for MPSO. The multi-objective fitness functions of MPSO minimize the motion and orientation of the DDER during navigation. Due to the minimization of motion and orientation, the DDER covers less distance to reach the goal and takes less time. The Two-Dimensional (2D) and Three-Dimensional (3D) navigation results of the DDER among the scattered obstacles have been presented in the V-REP software environment. The comparative analysis with previously developed Invasive Weed Optimization (IWO) algorithm has also been performed to show the effectiveness and efficiency of the proposed MPSO algorithm

An Efficient Approach for Line-Following Automated Guided Vehicles Based on Fuzzy Inference Mechanism

Recently, there has been increasing attention paid to AGV (Automated Guided Vehicle) in factories and warehouses to enhance the level of automation. In order to improve productivity, it is necessary to increase the efficiency of the AGV, including working speed and accuracy. This study presents a fuzzy-PID controller for improving the efficiency of a line-following AGV. A line-following AGV suffers from tracking errors, especially on curved paths, which causes a delay in the lap time. The fuzzy-PID controller in this study mimics the principle of human vehicle control as the situation-aware speed adjustment on curved paths. Consequently, it is possible to reduce the tracking error of AGV and improve its speed. Experimental results show that the Fuzzy-PID controller outperforms the PID controller in both accuracy and speed, especially the lap time of a line-following AGV is enhanced up to 28.6% with the proposed fuzzy-PID controller compared to that with the PID controller only

Fuzzy-based collision avoidance system for autonomous driving in complicated traffic scenarios

Collision avoidance is an important requirement for safe and autonomous driving in modern transportation system. In this paper, we present a fuzzy based control approach for smart and safe obstacle avoidance in complicated traffic scenario where there are static and dynamic obstacles (e.g. broken-down vehicles, wrong parking road-side vehicles, or moving vehicles, etc.) The fuzzy system makes an optimal decision to control the car throttle, braking, and steering to avoid collision using the available information on the road map (i.e. the distance to obstacles, the current traffic in the neighbouring lanes, the velocity of the front and rear car, etc.). Simulation results from three different scenarios involving a combination of dynamic and static or broken-down vehicles show that the fuzzy controlled car can effectively avoid obstacle or collision in complicated traffic situations. ©2018 IEEE