Context-aware task execution of mobile robots in collaborative smart environments

The main objective of my Ph.D. is to endow mobile robots with the capability to plan a task
execution strategy by leveraging on sensing and actuation functionality provided by other robots
and Internet of Things (IoT) devices. Mobile (semi-)autonomous robots are being used in an
increasing number of domains and must adapt their behaviour to cope with dynamic and
uncertain environments.
The first part of this PhD will look into how the capabilities and resources of agents (i.e. robots, IoT
sensors and IoT actuators) can be modelled and discovered. Agents will need to update the model
based on their current state. This will form a critical framework that the rest of the project will
build upon.
Using the sensing capabilities of different agents the robot will formulate a plan to execute a task.
As the plan is executed sections of the plan will be updated based on new sensor input. Combining
agents’ actuation capabilities will allow more demanding tasks to be executed, and increase the
system’s robustness. Multiple robots will collaborate to complete tasks, while trying to keep costs
(e.g. battery and CPU usage, and task execution time) to a minimum.
At each milestone experiments will be performed in real-world situations to evaluate how well the
algorithms produced cope in uncertain environments. Through these incremental experiments
data collection and analysis will be performed to allow conference and A1 journal publications to
be produced.