In partnership with CSIRO and Excellerate Australia, we’re developing several multi-agent cloud robotic systems that can be used in environments, such as school classrooms or to provide first aid assistance for elderly people.
The introduction of cloud robotics has merged the two ever-progressing domains of robotics and cloud computing. The added feature of cloud implies less dependence on human input and more support from ubiquitous resources. Industry 4.0 is envisioned to be a key area for infusion of these robotic technologies, especially in automating applications such as sensing, actuating and monitoring via insurgence of cloud computing and wireless sensors. Particularly, smart factories located in remote locations with challenges to health, safety and environment are our motivation for robotic inspection, maintenance and repair.
Cloud-aided robots can complement the sensors with action-oriented task, such as inspection, fault diagnosis and sensor testing. The tasks associated with these robotic applications are usually interdependent, latency sensitive and compute intensive. In addition, the resources are heterogeneous in terms of processing capability and energy consumption. These smart factory applications require robots to continuously update intensive data in order to execute tasks in a coordinated manner. Therefore, real-time requirements need to be fulfilled by tackling resource constraints.
Given the context, the specific aim of our research team is to design real-time resource allocation schemes to improve resource sharing among robots and optimise task offloading to the cloud for multi-agent cloud robotic systems. We’ve already implemented evolutionary approaches to make offloading decision for a single robot factory maintenance application in a sample oil factory workspace and optimal resource allocation for the tasks of emergency management service in a smart factory.
The research group has also worked on several proof of concepts, including the development of a cloud-aided robot hardware, face recognition, voice-based control, environment mapping and path planning via cloud-supported robot. We are also supporting elderly people with first aid assistance using Turtlebot 2.0, as well as integrating robot teaching assistance in a school classroom.
An example of a multi-agent cloud robotic system.