Control Of Wheeled Mobile Robots An Experimental Overview
Wheeled mobile robots (WMRs) are increasingly being used in various fields, including agriculture, surveillance, manufacturing, and exploration. These robots are designed to move on wheels and are capable of performing complex tasks. However, the control of WMRs remains a challenging problem, and researchers are constantly working to improve their performance.
What is a Wheeled Mobile Robot?
A wheeled mobile robot is a type of mobile robot that moves on wheels. These robots can be controlled remotely or autonomously and are used in various applications. The design of WMRs varies depending on the application, but most WMRs have a chassis, wheels, and a control system.
Control of Wheeled Mobile Robots
The control of WMRs involves the regulation of the robot's motion and orientation. The control system of a WMR typically includes sensors, actuators, and a controller. The sensors provide information about the robot's environment, while the actuators enable the robot to move and change its orientation. The controller processes the information from the sensors and actuates the robot to achieve the desired motion and orientation.
Experimental Overview
Researchers have conducted numerous experiments on the control of WMRs to improve their performance. These experiments involve the use of different control strategies, such as feedback control, fuzzy control, and neural network control. The experiments also involve the use of different sensors and actuators to improve the robot's perception and motion.
Feedback Control
Feedback control is a control strategy that uses feedback from the robot's sensors to adjust its motion and orientation. This strategy is widely used in the control of WMRs and has been shown to improve their performance. The feedback control strategy involves the use of a controller that receives information from the robot's sensors and generates control signals to adjust the robot's motion and orientation.
Fuzzy Control
Fuzzy control is a control strategy that uses fuzzy logic to adjust the robot's motion and orientation. This strategy is based on the idea that human reasoning is imprecise and uncertain, and it can be used to control WMRs in uncertain environments. Fuzzy control involves the use of a fuzzy controller that receives information from the robot's sensors and generates control signals based on fuzzy rules.
Neural Network Control
Neural network control is a control strategy that uses artificial neural networks to adjust the robot's motion and orientation. This strategy is based on the idea that the neural network can learn from the robot's environment and improve its performance. Neural network control involves the use of a neural network controller that receives information from the robot's sensors and generates control signals based on the network's learned behavior.
Sensors and Actuators
The sensors and actuators used in the control of WMRs vary depending on the application. The most commonly used sensors include cameras, sonars, and lidars, while the most commonly used actuators include motors and servos. The choice of sensors and actuators depends on the robot's task and environment.
Conclusion
The control of WMRs remains a challenging problem, but researchers are constantly working to improve their performance. The experiments conducted on WMRs have shown that different control strategies, such as feedback control, fuzzy control, and neural network control, can be used to improve their performance. The choice of sensors and actuators also plays a crucial role in the control of WMRs. As the field of robotics continues to evolve, it is likely that the control of WMRs will continue to improve.