Scalable Multi-Agent Systems in Restricted Environments
| dc.contributor.author | Heintzman, Larkin Lee | en |
| dc.contributor.committeechair | Williams, Ryan K. | en |
| dc.contributor.committeemember | Stilwell, Daniel J. | en |
| dc.contributor.committeemember | Abaid, Nicole | en |
| dc.contributor.committeemember | McClure, James E. | en |
| dc.contributor.committeemember | Wyatt, Chris L. | en |
| dc.contributor.department | Electrical Engineering | en |
| dc.date.accessioned | 2023-02-16T09:00:14Z | en |
| dc.date.available | 2023-02-16T09:00:14Z | en |
| dc.date.issued | 2023-02-15 | en |
| dc.description.abstract | Modern robotics demonstrates the reality of near sci-fi solutions regularly. Swarms of interconnected robotic agents have been proven to have benefits in scalability, robustness, and efficiency. In communication restricted environments, such teams of robots are often required to support their own navigation, planning, and decision making processes, through use of onboard processors and collaboration. Example scenarios that exhibit restriction include unmanned underwater surveys and robots operating in indoor or remote environments without cloud connectivity. We begin this thesis by discussing multi-agent state estimation and it's observability properties, specifically for the case of an agent-to-agent range measurement system. For this case, inspired by navigation requirements underwater, we derive several conditions under which the system's state is guaranteed to be locally weakly observable. Ensuring a state is observable is necessary to maintain an estimate of it via filters, thus observability is required to support higher level navigation and planning. We conclude this section by creating an observability-based planner to control a subset of the agents' inputs. For the next contribution, we discuss scalability for coverage maximizing path planners. Typically planning for many individual robots incurs significant computational complexity which increases exponentially with the number of agents, this is often exacerbated when the objective function is collaborative as in coverage optimization. To maintain feasibility while planning for a large team of robots, we call upon a powerful relation from combinatorics which utilizes the greedy selection algorithm and a matroid condition to create an efficient planner that maintains a fixed performance ratio when compared to the optimal path. We then introduce a motivating example of autonomously assisted search and rescues using multiple aerial agents, and derive planners and models to suit the application. The framework begins by estimating the likely locations of a lost person through a Monte Carlo simulation, yielding a heatmap covering the area of interest. The heatmap is then used in combination with parametrized agent trajectories and a machine learning optimization algorithm to maximize the search efficiency. The search and rescues use case provides an excellent computational testbed for the final portion of the work. We close by discussing a computation architecture to support multi-agent system autonomy. Modern robotic autonomy results, especially computer vision and machine learning algorithms, often require large amounts of processing to yield quality results. With general purpose computing devices reaching a progression barrier, one that is not expected to be solved in the near term, increasingly devices must be designed with their end purposes in mind. To better support autonomy in multi-agent systems, we propose to use a distributed cluster of embedded processors which allows the sharing of computation and storage resources among the component members with minimal communication overhead. Our proposed architecture is composed of mature softwares already well-known in the robotics community, Kubernetes and the robot operating system, allowing ease of use and interoperability with existing algorithms. | en |
| dc.description.abstractgeneral | The traditional approach of robotics typically uses a single large platform capable of accomplishing all tasks assigned to it. However, it has been discovered that deploying multiple smaller platforms, each with their own processor and specific expertise, can have massive performance benefits compared to previous approaches. This development has been driven largely by readily available computing and mobility hardware. Termed as multi-agent systems, they can excel in areas that benefit from multiple perspectives, simultaneous task execution, and redundancy. In addition, planning algorithms developed for previous approaches often can map well onto multi-agent systems, provided there is adequate computational support. In cases where network or cloud connectivity is limited, teams of agents must use their own processors and sensors to make decisions and communicate. However, often an individual agent's computing hardware is limited in mass or size, thus limiting it's processing capabilities. In this work we will first discuss several multi-agent system algorithms, starting with estimation and navigation and ending with area search. We then conclude the work by proposing a novel architecture designed to distribute the computation load across the team in a highly scalable way. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:36484 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/113838 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | Multi-Agent Systems | en |
| dc.subject | Path Planning | en |
| dc.subject | Agent-Based Systems | en |
| dc.title | Scalable Multi-Agent Systems in Restricted Environments | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Electrical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |