The project is investigating the sharing of control between human tele-operators and humans with sensor systems to assist during progressively more difficult mobile robot paths. That has included an analysis of the effects of time delays on the teleoperation of a mobile robot in various modes of operation and a consideration of vehicle safety systems.
The ability to complete simple tele-operated rescue or maintenance mobile-robot tasks has been compared when using a sensor system and without a sensor system.
Artificial neural networks were used to detect risk of vehicle failure and simple expert systems were created to improve on the use of ultrasonic sensor-systems and to adjust tele-operator learning when provided with different levels of sensor support. Finally new models of wheeled vehicles (and trailers) were created along with new non-model-based controllers for wheeled vehicles pulling up to two trailers.
As tasks become progressively more difficult then sensor systems can become more useful.
Time delays can have an adverse affect on performance.
Vehicle safety systems were explored and some simple prototypes were created.
Systems were demonstrated during simple tele-operated rescue and maintenance mobile-robot tasks.
Artificial neural networks were demonstrated detecting risk of vehicle failure.
Simple expert systems improved ultrasonic sensor-system performance.
Tele-operator learning was improved when sensor support was introduced while learning to drive mobile robots.
Non-model-based control of a wheeled vehicle pulling two trailers was demonstrated.
Short title | Mobile Robots |
---|
Status | Active |
---|
Effective start/end date | 15/10/09 → … |
---|
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):