In recent years, we have seen the applications of distributed autonomous mobile robots (DAMRs) in a broad spectrum of areas like search and rescue, disaster management, warehouse, and delivery systems. Although each type of systems employing DAMRs
has its specific challenges, they are all limited by energy since the robots are powered by batteries which have not advanced in decades. This motivates the development of energy efficiency for such systems.
Although there has been research on optimizing energy for robotic systems, their approaches are from low-level (e.g., mechanic, system control, or avionic) perspectives. They, therefore, are limited to a specific type of robots and not easily adjusted to apply for different types of robots. Moreover, there is a lack of work studying the problem from a software perspective and abstraction.
In this thesis, we tackle the problem from a software perspective and are particularly interested in DAMR systems in which a team of networked robots navigating in a physical environment and acting in concert to accomplish a common goal. Also, the primary focus of our work is to design schedules (or plans) for the robots so that they can achieve their goal while spending as little energy as possible. To this end, we study the problem in three different contexts:
- Managing reliability and energy consumption tradeoff. That is, we propose that robots verify computational results of one another to increase the corroboration of outputs of our DAMR systems. However, this new feature requires robots to do additional tasks and consume more energy. Thus, we propose approaches to reach a balance between energy consumption and the reliability of results obtained by our DAMR systems.
- Extending the operational time of robots. We first propose that our DAMR systems should employ charging stations where robots can come to recharge their batteries. Then, we aim to design schedules for the robots so that they can finish all their tasks while consuming as little energy and time (including the time spent for recharging) as possible. Moreover, we model the working space by a connected (possibly incomplete) graph to make the problem more practical.
- Coping with environmental changes. This path planning problem takes into account not only energy limits but also changes in the physical environment, which may result in overheads (i.e., additional time and energy) that robots incur while doing their tasks. To tackle the problem from a software perspective, we first utilize Gaussian Process and Polynomial Regression to model disturbances and energy consumption, respectively, then proposed techniques to generate plans
and adjust them when robots detect environmental changes.
For each problem, we give a formal description, a transformation to integer (linear) programming, online algorithms, and an online algorithm. Moreover, we also rigorously analyze the proposed techniques by conducting simulations and experiments in
a real network of unmanned aerial vehicles (UAVs). / Thesis / Candidate in Philosophy
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/24839 |
| Date | January 2019 |
| Creators | Vu, Anh-Duy |
| Contributors | Karakostas, George, Computing and Software |
| Source Sets | McMaster University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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