Optimal Trajectory Planning for Wheeled Mobile Robots under Localization Uncertainty and Energy Efficiency Constraints.

Xiaolong Zhang,Hui Wang,Yu Huang,Youmin Rong,Chao Liu,Gen Li

doi:10.3390/s21020335

Abstract

With the rapid development of robotics, wheeled mobile robots are widely used in smart factories to perform navigation tasks. In this paper, an optimal trajectory planning method based on an improved dolphin swarm algorithm is proposed to balance localization uncertainty and energy efficiency, such that a minimum total cost trajectory is obtained for wheeled mobile robots. Since environmental information has different effects on the robot localization process at different positions, a novel localizability measure method based on the likelihood function is presented to explicitly quantify the localization ability of the robot over a prior map. To generate the robot trajectory, we incorporate localizability and energy efficiency criteria into the parameterized trajectory as the cost function. In terms of trajectory optimization issues, an improved dolphin swarm algorithm is then proposed to generate better localization performance and more energy efficiency trajectories. It utilizes the proposed adaptive step strategy and learning strategy to minimize the cost function during the robot motions. Simulations are carried out in various autonomous navigation scenarios to validate the efficiency of the proposed trajectory planning method. Experiments are performed on the prototype “Forbot” four-wheel independently driven-steered mobile robot; the results demonstrate that the proposed method effectively improves energy efficiency while reducing localization errors along the generated trajectory.

Highlights

In the intelligent manufacturing field, wheeled mobile robots are one of the most widely used groups of robots in factories and warehouses to perform material transportation tasks that benefit from their automation and efficiency [1]
To make the comparison more clearly and directly, we provide th tion, localization error, travel distance, and total cost of the experimen be seen from the table, the proposed trajectory planning method ob sively-optimized trajectory with guaranteed minimum total cost
This paper proposed an optimal trajectory planning method to obtain a minimum total cost trajectory for wheeled mobile robots by balancing localization errors and energy efficiency

Summary

Introduction

In the intelligent manufacturing field, wheeled mobile robots are one of the most widely used groups of robots in factories and warehouses to perform material transportation tasks that benefit from their automation and efficiency [1]. A series of trajectory planning schemes have been reported until now, such as the graph search-based method [2,3], interpolating curve planning method [4,5], sampling-based planning method [6], and numerical optimization method [7] Among these methods, the interpolating curve planning method is a widely examined planning strategy due to its optimized performance and strong ability to handle external constraints. In [12], the authors propose a novel chaotic grouping particle swarm optimization algorithm with a dynamic regrouping strategy to solve complex numerical optimization problems. In [14], the authors first propose a dolphin swarm algorithm (DSA) based on the biological characteristics and living habits of dolphins to solve the optimization problem with first-slow--fast convergence. In [16], the authors optimize the localization process by using DSA to process the sensed data in Wireless Sensor Networks

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