Ben Grocholsky Doctor of Philosophy The University of Sydney March 2002 Information-Theoretic Control of This thesis is concerned with the development of a consistent, information-theoretic basis for understanding of coordination and cooperation decentralised multi-sensor multi-platform systems. Autonomous systems composed of multiple sensors and multiple platforms potentially have significant importance in applications such as defence, search and rescue, mining or intelligent manufacturing. However, the e#ective use of multiple autonomous systems requires that an understanding be developed of the mechanisms of coordination and cooperation between component systems in pursuit of a common goal. A fundamental, quantitative, understanding of coordination and cooperation between decentralised autonomous systems is the main goal of this thesis.

We describe the design and experimental validation of a large heterogeneous mobile robot team built for the DARPA Software for Distributed Robotics (SDR) program. The core challenge for the SDR program was to develop a multi-robot system capable of carrying out a specific mission: to deploy a large number of robots into an unexplored building, map the building interior, detect and track intruders, and transmit all of the above information to a remote operator. To satisfy these requirements, we developed a heterogeneous robot team consisting of approximately 80 robots. We sketch the key technical elements of this team, focusing on the novel aspects, and present selected results from supervised experiments conducted in a 600 m 2 indoor environment. 1

We describe a set of distributed algorithms used to disperse a large group of autonomous mobile robots efficiently throughout an indoor environment. Only local inter-robot communication and processing is used. Ad-hoc communications network topologies formed by gradient floods spread messages and guide robot motion. Special attention has been given to doors, hallways, and other constrictions. The network maintains a route to chargers to allow self-charging. 1

Abstract. The control of robot swarming in a distributed manner is a difficult problem because global behaviors must emerge as a result of many local actions. This paper uses a bio-inspired control method called the Digital Hormone Model (DHM) to control the tasking and executing of robot swarms based on local communication, signal propagation, and stochastic reactions. The DHM model is probabilistic, dynamic, fault-tolerant, computationally efficient, and can be easily tasked to change global behavior. Different from most existing distributed control and learning mechanisms, DHM considers the topological structure of the organization, supports dynamic reconfiguration and self-organization, and requires no globally unique identifiers for individual robots. The paper describes the DHM and presents the experimental results on simulating biological observations in the forming of feathers, and simulating wireless communicated swarm behavior at a large scale for attacking target, forming sensor networks, self-repairing, and avoiding pitfalls in mission execution.

We study groups of autonomous robots engaged in a foraging task as typically found in some ant colonies. The task is to find a prey object and a nest object, establish a path between the two, and transport the prey to the nest. Once a path is established, robots are recruited to the prey, self-assemble into a pulling structure and collectively transport the prey---which is too heavy for a single robot to move it--- along the path to the nest. We follow a swarm-intelligence based control approach.

This paper presents an approach for deploying a team of mobile sensor nodes to form a sensor network in indoor environments. The challenge in this work is that the mobile sensor nodes have no ability for localization or obstacle avoidance. Thus, our approach entails the use of more capable "helper" robots that "herd" the mobile sensor nodes into their deployment positions. To extensively explore the issues of heterogeneity in multi-robot teams, we employ the use of two types of helper robots -- one that acts as a leader and a second that: 1) acts as a follower and 2) autonomously teleoperates the mobile sensor nodes. Due to limited sensing capabilities, neither of these helper robots can herd the mobile sensor nodes alone; instead, our approach enables the team as a whole to successfully accomplish the sensor deployment task. Our approach involves the use of line-of-sight formation keeping, which enables the follower robot to use visual markers to move the group along the path executed by the leader robot. We present results of the implementation of this approach in simulation, as well as results to date in the implementation on physical robot systems. To our knowledge, this is the first implementation of robot herding using such highly heterogeneous robots, in which no single type of robot could accomplish the sensor network deployment task, even if multiple copies of that robot type were available.

In this paper, we consider the problem of placing networked sensors in a way that guarantees coverage and connectivity. We focus on sampling based deployment and present algorithms that guarantee coverage and connectivity with a small number of sensors. We consider two different scenarios based on the flexibility of deployment. If deployment has to be accomplished in one step, like airborne deployment, then the main question becomes how many sensors are needed. If deployment can be implemented in multiple steps, then awareness of coverage and connectivity can be updated. For this case, we present incremental deployment algorithms which consider the current placement to adjust the sampling domain. The algorithms are simple, easy to implement, and require a small number of sensors. We believe the concepts and algorithms presented in this paper will provide a unifying framework for existing and future deployment algorithms which consider many practical issues not considered in the present work.

Abstract. Robotics researchers have studied robots that can follow trails laid by other robots. We, on the other hand, study robots that leave trails in the terrain to cover closed terrain repeatedly. How to design such ant robots has so far been studied only theoretically for gross robot simplifications. In this article, we describe for the first time how to build physical ant robots that cover terrain and test their design both in realistic simulation environments and on a Pebbles III robot. We show that the coverage behavior of our ant robots can be modeled with a modified version of node counting, a real-time search method. We then report on first experiments that we performed to understand their efficiency and robustness in situations where some ant robots fail, they are moved without realizing this, the trails are of uneven quality, and some trails are destroyed. Finally, we report the results of a large-scale simulation experiment where ten ant robots covered a factory floor of 25 by 25 meters repeatedly over 85 hours without getting stuck. 1

Human interaction with large numbers of robots or distributed sensors presents a number of difficult challenges including supervisory management, monitoring of individual and collective state, and apprehending situation awareness. A rich source of information about the environment can be provided even with robots that have no explicit representations or maps of their locale. To do this, we transform a robot swarm into a distributed interface embedded within the environment. Visually, each robot acts like a pixel within a much larger visual display space so that any robot need only communicate a small amount of information from its current location. Our approach uses Augmented Reality techniques for communicating information to humans from large numbers of small-scale robots to enable situation awareness, monitoring, and control for surveillance, reconnaissance, hazard detection, and path finding.

We present two swarm intelligence control mechanisms used for distributed robot path formation. In the first, the robots form linear chains. We study three variants of robot chains, which vary in the degree of motion allowed to the chain structure. The second mechanism is called vectorfield. In this case, the robots form a pattern that globally indicates the direction towards a goal or home location. We test each controller on a task that consists in forming a path between two objects which an individual robot cannot perceive simultaneously. Our simulation experiments show promising results. All the controllers are able to form paths in complex obstacle environments and exhibit very good scalability, robustness, and fault tolerance characteristics. Additionally, we observe that chains perform better for small robot group sizes, while vectorfield performs better for large groups.