A Study on the Effect of Parameters for ROS Motion Planer and Navigation System for Indoor Robot

Abstract

In the past decades, the service robot industry had risen rapidly. The office assistant robot is one type of service robot used to assist officers in an office environment. For the robot to navigate autonomously in the office, navigation algorithms and motion planners were implemented on these robots. Robot Operating System (ROS) is one of the common platforms to develop these robots. The parameters applied to the motion planners will affect the performance of the Robot. In this study, the global planners, A* and Dijkstra algorithm and local planners, Dynamic Window Approach (DWA) and Time Elastic Band (TEB) algorithms were implemented and tested on a robot in simulation and a real environment. Results from the experiments were used to evaluate and compare the performance of the robot with different planners and parameters. Based on the results obtained, the global planners, A* and Dijkstra algorithm both can achieve the required performance for this application whereas TEB outperforms DWA as the local planner due to its feasibility in avoiding dynamic obstacles in the experiments conducted.

Optimal Local Planner Selection for Autonomous Robots Using Machine Learning

“…Even though there have been many studies and ongoing research related to the local planner for mobile robots, selecting the most appropriate local planner for an environment remains a challenge. In [4], the aim is to select the most suitable combination of local and global planners for an office assistant robot to be used in the office, by examining the results of multiple tests conducted with different parameters. However, it should be noted that if the office is changed or the arrangement of objects is altered, this combination may not be the optimal choice.…”

“…Looi and Ng [4] conducted a study to determine the best global and local planner for an indoor office robot. They compared the effects of A* and Dijkstra algorithms as global planners and Dynamic Window Approach (DWA) and Timed Elastic Band (TEB) methods as local planners on the robot's movement.…”

Optimal path planning using bidirectional rapidly-exploring random tree star-dynamic window approach (BRRT*-DWA) with adaptive Monte Carlo localization (AMCL) for mobile robot

“…Other authors proposed DWA for different applications [14][15][16]. Ferrer Sánchez [17], and Looi et al [18], compare the local planners (controllers) Time Elastic Band (TEB), Model Predictive Control (MPC), and Dynamic Window Approach (DWA), and give the insight and applicability of each method, which led to the choice of DWA for our system.…”

Solving Optimal Path Planning Problem of an Intelligent Mobile Robot in Dynamic Environment Using Bidirectional Rapidly-exploring Random Tree Star-Dynamic Window Approach (BRRT*-DWA) with Adaptive Monte Carlo Localization (AMCL)

“…Other authors proposed DWA for different applications [13][14][15]. Ferrer Sánchez J [16], and Looi et al [10], compare the local planners (controllers) Time Elastic Band (TEB), Model Predictive Control (MPC), and Dynamic Window Approach (DWA), and give the insight and applicability of each method which led to the choosing of DWA to our system. Redmon et al [17] present YOLO, a new approach to object detection.…”