A Novel Pure Pursuit Algorithm for Autonomous Vehicles Based on Salp Swarm Algorithm and Velocity Controller

Abstract

Pure pursuit algorithm is one of the most effective ways of path tracking in autonomous vehicles. Nevertheless, the tracking accuracy of the existing pure pursuit algorithm is limited by the look-ahead distance. In this paper, to improve the tracking accuracy of the pure pursuit algorithm, a novel pure pursuit algorithm based on the optimized look-ahead distance named OLDPPA is proposed. Four improvements are presented in OLDPPA. Firstly, to find a better look-ahead distance of pure pursuit algorithm, salp swarm algorithm (SSA) is used in pure pursuit algorithm. Secondly, Brownian motion, a random motion mechanism of particles, is introduced in SSA to enhance its exploitation and exploration capabilities. Thirdly, to accelerate the convergence speed of SSA, a weighted mechanism which uses two different weights in the search process to adjust the salps closer to the food source quickly is assigned. Based on innovations 2 and 3, adaptive Brownian motion salp swarm algorithm (ABMSSA) is proposed and applied to pure pursuit algorithm. Finally, a velocity controller which outputs the speed of the next moment according to the distance and time interval between the look-ahead point and the current vehicle position is designed in OLDPPA, to ensure that the vehicle reaches its destination at a specified time. To verify the effectiveness and efficiency of OLDPPA, OLDPPA is applied in four different paths and the corresponding results are compared with other pure pursuit algorithms that use different look-ahead distances. Experimental results show that the tracking accuracy of OLDPPA is better than other algorithms.

Keywords

• Pure pursuit algorithm
• autonomous vehicles
• salp swarm algorithm
• velocity controller
• optimized look-ahead distance