Abstract
SUMMARYThis paper tackles the problem of motion planning and control of a car-like robot in an obstacle-ridden workspace. A kinematic model of the vehicle, governed by a homogeneous system of first-order differential equations, is used. A solution to the multi-tasking problem of target convergence, obstacle avoidance, and posture control is then proposed. The approach of solving the problem is two-fold. Firstly, a novel velocity algorithm is proposed to drive the car-like robot from its initial position to the target position. Secondly, a single layer artificial neural network is trained to avoid disc-shaped obstacles and provide corresponding weights, which are then used to develop a function for the steering angles. Thus, our method does not need a priori knowledge of the environment except for the goal position. With the help of the Direct Method of Lyapunov, it is shown that the proposed forms of the velocity and steering angle ensure point stability. For posture stability, we model the two parallel boundaries of a row-structured parking bay as continua of disk-shaped obstacles. Thus, our method is extendable to ensuring posture stability, which gives the desired final orientation. Computer simulations of the generated path are presented to illustrate the effectiveness of the method.
Highlights
Motion planning and control of autonomous nonholonomic robot has been an active area of research over the past two decades
While neural network-based approach of controlling the motion of nonholonomic robots is commonly found in literature, the following aspects are lacking: (a) a rigorous stability analysis of a system that depends on an artificial neural network (ANN) to provide the necessary parameters; (b) the inclusion of a target when designing the velocity algorithm; (c) an analysis of posture control of the nonholonomic robots; and (d) an explicit formula of the steering obtained via an ANN
Concluding Remarks This paper proposes a solution to the motion planning and control problem of a car-like robot
Summary
Motion planning and control of autonomous nonholonomic robot has been an active area of research over the past two decades. The motion was controlled in an obstacle-ridden workspace, the authors have not considered the target convergence or the velocity of the robots. (b) the inclusion of a target when designing the velocity algorithm; (c) an analysis of posture control of the nonholonomic robots; and (d) an explicit formula of the steering obtained via an ANN. The authors designed a velocity algorithm that could drive the robot from its initial position to the target position and used a multi-layer perceptron (MLP) to control the direction of a point mass robot in the obstacle region. [20], we will model the steering angle of the car-like robot in the obstacle-ridden workspace using a single-layer perceptron (SLP) and derive an explicit formula of the steering angle in terms of specific inputs. V and φ are treated as controllers that will be designed in later sections
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