Publication Date

2017-04-20

Availability

Open access

Embargo Period

2017-04-20

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PHD)

Department

Mechanical Engineering (Engineering)

Date of Defense

2017-04-03

First Committee Member

Ryan Karkkainen

Second Committee Member

Amir Rahmani

Third Committee Member

Kamal Premaratne

Fourth Committee Member

Manohar Murthi

Abstract

Several biological examples show that living organisms cooperate to collectively accomplish tasks impossible for single individuals. More importantly, this coordination is often achieved with a very limited set of information. Inspired by these observations, research on autonomous systems has focused on the development of distributed control techniques for control and guidance of groups of autonomous mobile agents, or robots. From an engineering perspective, when coordination and cooperation is sought in large ensembles of robotic vehicles, a reduction in hardware and algorithms’ complexity becomes mandatory from the very early stages of the project design. The research for solutions capable of lowering power consumption, cost and increasing reliability are thus worth investigating. In this work, we studied low-complexity techniques to achieve cohesion and control on swarms of autonomous robots. Starting from an inspiring example with two-agents, we introduced effects of neighbors’ relative positions on control of an autonomous agent. The extension of this intuition addressed the control of large ensembles of autonomous vehicles, and was applied in the form of a herding-like technique. To this end, a low-complexity distance-based aggregation protocol was defined. We first showed that our protocol produced a cohesion aggregation among the agent while avoiding inter-agent collisions. Then, a feedback leader-follower architecture was introduced for the control of the swarm. We also described how proximity measures and probability of collisions with neighbors can also be used as source of information in highly populated environments.

Keywords

Multi-agent robotics, Low Complexity Control, Sense and Avoid, Networks Control

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