Shared Control in Robot Teleoperation With Improved Potential Fields

Abstract

In shared control teleoperation, the robot assists the user in accomplishing the desired task. Rather than simply executing the user's command, the robot attempts to integrate it with information from the environment, such as obstacle and/or goal locations, and it modifies its behavior accordingly. In this article, we propose a real-time shared control teleoperation framework based on an artificial potential field approach improved by the dynamic generation of escape points around the obstacles. These escape points are virtual attractive points in the potential field that the robot can follow to overcome the obstacles more easily. The selection of which escape point to follow is done in real time by solving a soft-constrained problem optimizing the reaching of the most probable goal, estimated from the user's action. Our proposal has been extensively compared with two state-of-theart approaches in a static cluttered environment and a dynamic setup with randomly moving objects. Experimental results showed the efficacy of our method in terms of quantitative and qualitative metrics. For example, it significantly decreases the time to complete the tasks and the user's intervention, and it helps reduce the failure rate. Moreover, we received positive feedback from the users that tested our proposal. Finally, the proposed framework is compatible with both mobile and manipulator robots.

A Systematic Comparison of Simulation Software for Robotic Arm Manipulation using ROS2

“…Lin et al and Gottardi et al conducted a user survey and confirmed that users preferred the shared autonomy approach, as it reduced complexity and mental strain. Furthermore, [25] observed results similar to [8], who hypothesised that users preferred to give up control if it meant increasing the task completion rate. However, Javdani et al [8], [26] have falsified this hypothesis using a system similar to [11], [4], [25].…”

“…Furthermore, [25] observed results similar to [8], who hypothesised that users preferred to give up control if it meant increasing the task completion rate. However, Javdani et al [8], [26] have falsified this hypothesis using a system similar to [11], [4], [25]. The authors concluded that users preferred to lose control if it meant an increase in a task's success rate only for a more complex task, while, for simple tasks, users still preferred to have more control.…”

“…Recently, Gottardi et al [25] have investigated combining multiple control systems, such as a VR controller and a tracking band on the upper arm, to track the user's movements. Rakita et al [6] also compared various control methods; a stylus, a touchscreen, and a VR controller.…”

“…For example, Chen et al [10] created a system in which the operator, using a joystick, manipulated the robot's end effector to an object, and then the robot could either grasp the object autonomously or assist the user in fine-tuning the robot position for a more optimal grasp. Later, [11], [4], [25] built on [10] where the user teleoperated the robots directly to a planned position but allowed the robot to perform the grasp automatically or, in the case of [4], turn a valve handle. Lin et al and Gottardi et al conducted a user survey and confirmed that users preferred the shared autonomy approach, as it reduced complexity and mental strain.…”

Path Planning of a Multifunctional Elderly Intelligent Wheelchair Based on the Sensor and Fuzzy Bayesian Network Algorithm

“…Relevant scholars combined the DWA algorithm with the Ackerman robot to realize the autonomous obstacle avoidance function of the Ackerman robot. Related scholars applied the least squares method to DWA-based path planning, which improved the accuracy of robot navigation in unknown environments [23][24][25].…”