Gregory Dudek

A Taxonomy for Multi-Agent Robotics*

A key difficulty in the design of multi-agent robotic systems is the size and complexity of the space of possible designs. In order to make principled design decisions, an understanding of the many possible system configurations is essential. To this end, we present a taxonomy that classifies multi-agent systems according to communication, computational and other capabilities. We survey existing efforts involving multi-agent systems according to their positions in the taxonomy. We also present additional results concerning multi-agent systems, with the dual purposes of illustrating the usefulness of the taxonomy in simplifying discourse about robot collective properties, and also demonstrating that a collective can be demonstrably more powerful than a single unit of the collective.

Robotic exploration as graph construction

Addressed is the problem of robotic exploration of a graphlike world, where no distance or orientation metric is assumed of the world. The robot is assumed to be able to autonomously traverse graph edges, recognize when it has reached a vertex, and enumerate edges incident upon the current vertex relative to the edge via which it entered the current vertex. The robot cannot measure distances, and it does not have a compass. It is demonstrated that this exploration problem is unsolvable in general without markers, and, to solve it, the robot is equipped with one or more distinct markers that can be put down or picked up at will and that can be recognized by the robot if they are at the same vertex as the robot. An exploration algorithm is developed and proven correct. Its performance is shown on several example worlds, and heuristics for improving its performance are discussed. >

Multi-robot collaboration for robust exploration

This paper presents a new sensing modality for multirobot exploration. The approach is based on using a pair of robots that observe each other, and act in concert to reduce odometry errors. We assu...This paper presents a new sensing modality for multirobot exploration. The approach is based on using a pair of robots that observe each other, and act in concert to reduce odometry errors. We assume the robots can both directly sense nearby obstacles and see each other. The proposed approach improves the quality of the map by reducing the inaccuracies that occur over time from dead reckoning errors. Furthermore, by exploiting the ability of the robots to see each other, we can detect opaque obstacles in the environment independently of their surface reflectance properties. Two different algorithms, based on the size of the environment, are introduced, with a complexity analysis, and experimental results in simulation and with real robots.

Multi-robot cooperative localization: a study of trade-offs between efficiency and accuracy

This paper examines the tradeoffs between different classes of sensing strategy and motion control strategy in the context of terrain mapping with multiple robots. We consider a larger group of robots that can mutually estimate one anothers position (in 2D or 3D) and uncertainty using a sample-based (particle filter) model of uncertainty. Our prior work has dealt with a pair of robots that estimate one anothers position using visual tracking and coordinated motion. Here we extend these results and consider a richer set of sensing and motion options. In particular, we focus on issues related to confidence estimation for groups of more than two robots.

A taxonomy for swarm robots

In many cases several mobile robots (autonomous agents) can be used together to accomplish tasks that would be either more difficult or impossible for a robot acting alone. Many different models have been suggested for the makeup of such collections of robots. In this paper the authors present a taxonomy of the different ways in which such a collection of autonomous robotic agents can be structured. It is shown that certain swarms provide little or no advantage over having a single robot, while other swarms can obtain better than linear speedup over a single robot. There exist both trivial and non-trivial problems for which a swarm of robots can succeed where a single robot will fail. Swarms are more than just networks of independent processors - they are potentially reconfigurable networks of communicating agents capable of coordinated sensing and interaction with the environment.

Shape Representation and Recognition from Multiscale Curvature

We present a technique for shape representation and the recognition of objects based on multiscale curvature information. It provides a single framework for both the decomposition and recognition of both planar curves as well as surfaces in three-dimensional space. The decomposition operation simultaneously performs data interpolation, data smoothing, and segmentation. The unification of these three stages results in a smoothing operation that is coupled with the primitives to be used in description. Each of the minimization operators, in addition to having a curvature tuning, also has a different spatial sensitivity function. As a result, the different possible descriptions capture information at multiple spatial scales. This allows a single region of an object to be described in more than one way, when appropriate. The practicality of the ensuing representation is demonstrated by the recognition of planar curves. A matching strategy based on dynamic programming is used. The results illustrate the manner in which a continuous spectrum of similar objects can be defined, ranging from those that are very similar to a target to those that are very different from it.

Collaborative Robot Exploration and Rendezvous: Algorithms, Performance Bounds and Observations

We consider the problem of how two heterogeneous robots can arrange to meet in an unknown environment from unknown starting locations: that is, the problem of arranging a robot rendezvous. We are interested, in particular, in allowing two robots to rendezvous so that they can collaboratively explore an unknown environment. Specifically, we address the problem of how a pair of exploring agents that cannot communicate with one another over long distances can meet if they start exploring at different unknown locations in an unknown environment.We propose several alternative algorithms that robots could use in attempting to rendezvous quickly while continuing to explore. These algorithms exemplify different classes of strategy whose relative suitability depends on characteristics of the problem definition. We consider the performance of our proposed algorithms analytically with respect to both expected- and worst-case behavior. We then examine their behavior under a wider set of conditions using both numerical analysis and also a simulation of multi-agent exploration and rendezvous. We examine the exploration speed, and show that a multi-robot system can explore an unknown environment faster than a single-agent system, even with the constraint of performing rendezvous to allow communication.We conclude with a demonstration of rendezvous implemented on a pair of actual robots.

AQUA: An Amphibious Autonomous Robot

AQUA, an amphibious robot that swims via the motion of its legs rather than using thrusters and control surfaces for propulsion, can walk along the shore, swim along the surface in open water, or walk on the bottom of the ocean. The vehicle uses a variety of sensors to estimate its position with respect to local visual features and provide a global frame of reference

A visually guided swimming robot

We describe recent results obtained with AQUA, a mobile robot capable of swimming, walking and amphibious operation. Designed to rely primarily on visual sensors, the AQUA robot uses vision to navigate underwater using servo-based guidance, and also to obtain high-resolution range scans of its local environment. This paper describes some of the pragmatic and logistic obstacles encountered, and provides an overview of some of the basic capabilities of the vehicle and its associated sensors. Moreover, this paper presents the first ever amphibious transition from walking to swimming.

Learning visual landmarks for pose estimation

We present an approach to vision-based mobile robot localization, even without an a-priori pose estimate. This is accomplished by learning a set of visual features called image-domain landmarks. The landmark learning mechanism is designed to be applicable to a wide range of environments. Each landmark is detected as a focal extremum of a measure of uniqueness and represented by an appearance-based encoding. Localization is performed using a method that matches observed landmarks to learned prototypes and generates independent position estimates for each match. The independent estimates are then combined to obtain a final position estimate, with an associated uncertainty. Quantitative experimental evidence is presented that demonstrates that accurate pose estimates can be obtained, despite changes to the environment.