Evaluation of the Path-Tracking Accuracy of a Three-Wheeled Omnidirectional Mobile Robot Designed as a Personal Assistant

This paper presents the empirical evaluation of the path-tracking accuracy of a three-wheeled omnidirectional mobile robot that is able to move in any direction while simultaneously changing its orientation. The mobile robot assessed in this paper includes a precise onboard LIDAR for obstacle avoidance, self-location and map creation, path-planning and path-tracking. This mobile robot has been used to develop several assistive services, but the accuracy of its path-tracking system has not been specifically evaluated until now. To this end, this paper describes the kinematics and path-planning procedure implemented in the mobile robot and empirically evaluates the accuracy of its path-tracking system that corrects the trajectory. In this paper, the information gathered by the LIDAR is registered to obtain the ground truth trajectory of the mobile robot in order to estimate the path-tracking accuracy of each experiment conducted. Circular and eight-shaped trajectories were assessed with different translational velocities. In general, the accuracy obtained in circular trajectories is within a short range, but the accuracy obtained in eight-shaped trajectories worsens as the velocity increases. In the case of the mobile robot moving at its nominal translational velocity, 0.3 m/s, the root mean square (RMS) displacement error was 0.032 m for the circular trajectory and 0.039 m for the eight-shaped trajectory; the absolute maximum displacement errors were 0.077 m and 0.088 m, with RMS errors in the angular orientation of 6.27° and 7.76°, respectively. Moreover, the external visual perception generated by these error levels is that the trajectory of the mobile robot is smooth, with a constant velocity and without perceiving trajectory corrections.

Development of Automatic Chair Transport System - Chair Recognition and Approach Strategy –

In schools and other educational institutions, there are many instances that require the arrangement of chairs depending on the required purpose, such as a class or event. Some chairs are on casters; however, educational institutions typically use stacking or folding chairs. Although effective in terms of storage, these must be lifted by hand during transportation, increasing the burden on the workforce. While automation of baggage transport in warehouses has improved significantly, little attention has been paid to the automation of chair transport. Despite the demand and the fact that self-propelled chairs have already been developed, automatic transport of chairs without casters has never been reported. In this study, we constructed an automatic chair-transport system using an omnidirectional mobile robot and focused on a stacking chair that allowed the robot to position itself underneath. The developed system utilizes the image of the seat and frame pipe of the stacking chair to estimate the chair’s position and direction with respect to the robot. Once the robot has positioned itself under the chair, the chair is lifted and transported using a lifter device attached to the robot.

Application of Deep Reinforcement Learning for Tracking Control of 3WD Omnidirectional Mobile Robot

Deep reinforcement learning, the fastest growing technique, to solve real-world complex problems by creatinga simple mathematical framework. It includes an agent, action, environment, and a reward. An agent will interactwith the environment, takes an optimal action aiming to maximize the total reward. This paper proposesthe compelling technique of deep deterministic policy gradient for solving the complex continuous actionspace of 3-wheeled omnidirectional mobile robots. Three-wheeled Omnidirectional mobile robots tracking isa difficult task because of the orientation of the wheels which makes it rotate around its own axis rather tofollow the trajectory. A deep deterministic policy gradient (DDPG) algorithm has been designed to train in environmentswith continuous action space to follow the trajectory by training the neural networks defined forthe policy and value function to maximize the reward function defined for the tracking of the trajectory. DDPGagent environment is created in the Reinforcement learning toolbox in MATLAB 2019 while for Actor and criticnetwork design deep neural network designer is used. Results are shown to illustrate the effectiveness of thetechnique with a convergence of error approximately to zero.