7 research outputs found
Efficient PID Controller based Hexapod Wall Following Robot
This paper presents a design of wall followingbehaviour for hexapod robot based on PID controller. PIDcontroller is proposed here because of its ability to controlmany cases of non-linear systems. In this case, we proposed aPID controller to improve the speed and stability of hexapodrobot movement while following the wall. In this paper, PIDcontroller is used to control the robot legs, by adjusting thevalue of swing angle during forward or backward movement tomaintain the distance between the robot and the wall. Theexperimental result was verified by implementing the proposedcontrol method into actual prototype of hexapod robot
Efficient PID Controller based Hexapod Wall Following Robot
This paper presents a design of wall following
behaviour for hexapod robot based on PID controller. PID
controller is proposed here because of its ability to control
many cases of non-linear systems. In this case, we proposed a
PID controller to improve the speed and stability of hexapod
robot movement while following the wall. In this paper, PID
controller is used to control the robot legs, by adjusting the
value of swing angle during forward or backward movement to
maintain the distance between the robot and the wall. The
experimental result was verified by implementing the proposed
control method into actual prototype of hexapod robot
Neuro-Fuzzy based Approach for Inverse Kinematics Solution of Industrial Robot Manipulators
Obtaining the joint variables that result in a desired position of the robot end-effector called as inverse kinematics is one of the most important problems in robot kinematics and control. As the complexity of robot increases, obtaining the inverse kinematics solution requires the solution of non linear equations having transcendental functions are difficult and computationally expensive. In this paper, using the ability of ANFIS (Adaptive Neuro-Fuzzy Inference System) to learn from training data, it is possible to create ANFIS, an implementation of a representative fuzzy inference system using a BP neural network-like structure, with limited mathematical representation of the system. Computer simulations conducted on 2 DOF and 3DOF robot manipulator shows the effectiveness of the approach