The RoboFlag SURF competition: results, analysis, and future work

The culmination of the 2002 RoboFlag Summer Undergraduate Research Fellowship program, jointly operated between California Institute of Technology and Cornell University, was a final competition between two teams of three undergraduate researchers. After ten weeks of preparation, Team Pasadena defeated Team Ithaca in two of the three final games. This paper provides the detailed results of the competition, an analysis of the competition, and reviews the future work

Human Management of the Hierarchical System for the Control of Multiple Mobile Robots

In order to take advantage of autonomous robotic systems, and yet ensure successful completion of all feasible tasks, we propose a mediation hierarchy in which an operator can interact at all system levels. Robotic systems are not robust in handling un-modeled events. Reactive behaviors may be able to guide the robot back into a modeled state and to continue. Reasoning systems may simply fail. Once a system has failed it is difficult to re-start the task from the failed state. Rather, the rule base is revised, programs altered, and the task re-tried from the beginning

Robust Hypothesis Testing and Statistical Color Classification (Dissertation)

The purpose of this research is twofold: (i) the development of a mathematical model for statistical color classification; and (ii) the testing of this model under controlled conditions. We consider the following hypothesis testing problem: Let Z = θ + V, where the scalar random variable Z denotes the sampling model, θ ∈ Ω is a location parameter, Ω ⊂ R, and V is additive noise with cumulative distribution function F. We assume F is uncertain, i.e., F ∈ F, where F denotes a given uncertainty class of absolutely continuous distributions with a parametric or semiparametric description. The null hypothesis is H0: θ ∈ Ω, F ∈ F and the alternative hypothesis is H1: θ ∉ Ω, F ∈ F. Through controlled testing we show that this model may be used to statistically classify colors. The color spectrum we use in these experiments is the Munsell color system which combines the three qualities of color sensation: Hue, Chroma and Value. The experiments show: (i) The statistical model can be used to classify colors in the Munsell color system; (ii) more robust results are achieved by using a Chroma-Hue match instead of a Perfect match; (iii) additional robustness can be achieved by classifying a color based on measurements averaged over a neighborhood of pixels versus measurements at a single pixel; and (iv) a larger color spectrum than the Munsell color system is needed to classify a range of man-made and natural objects

The Viability of Domain Constrained Coalition Formation for Robotic Collectives

Applications, such as military and disaster response, can benefit from robotic collectives' ability to perform multiple cooperative tasks (e.g., surveillance, damage assessments) efficiently across a large spatial area. Coalition formation algorithms can potentially facilitate collective robots' assignment to appropriate task teams; however, most coalition formation algorithms were designed for smaller multiple robot systems (i.e., 2-50 robots). Collectives' scale and domain-relevant constraints (i.e., distribution, near real-time, minimal communication) make coalition formation more challenging. This manuscript identifies the challenges inherent to designing coalition formation algorithms for very large collectives (e.g., 1000 robots). A survey of multiple robot coalition formation algorithms finds that most are unable to transfer directly to collectives, due to the identified system differences; however, auctions and hedonic games may be the most transferable. A simulation-based evaluation of three auction and hedonic game algorithms, applied to homogeneous and heterogeneous collectives, demonstrates that there are collective compositions for which no existing algorithm is viable; however, the experimental results and literature survey suggest paths forward.Comment: 46 pages, 9 figures, Swarm Intelligence (under review

GRAPE-S: Near Real-Time Coalition Formation for Multiple Service Collectives

Robotic collectives for military and disaster response applications require coalition formation algorithms to partition robots into appropriate task teams. Collectives' missions will often incorporate tasks that require multiple high-level robot behaviors or services, which coalition formation must accommodate. The highly dynamic and unstructured application domains also necessitate that coalition formation algorithms produce near optimal solutions (i.e., >95% utility) in near real-time (i.e., <5 minutes) with very large collectives (i.e., hundreds of robots). No previous coalition formation algorithm satisfies these requirements. An initial evaluation found that traditional auction-based algorithms' runtimes are too long, even though the centralized simulator incorporated ideal conditions unlikely to occur in real-world deployments (i.e., synchronization across robots and perfect, instantaneous communication). The hedonic game-based GRAPE algorithm can produce solutions in near real-time, but cannot be applied to multiple service collectives. This manuscript integrates GRAPE and a services model, producing GRAPE-S and Pair-GRAPE-S. These algorithms and two auction baselines were evaluated using a centralized simulator with up to 1000 robots, and via the largest distributed coalition formation simulated evaluation to date, with up to 500 robots. The evaluations demonstrate that auctions transfer poorly to distributed collectives, resulting in excessive runtimes and low utility solutions. GRAPE-S satisfies the target domains' coalition formation requirements, producing near optimal solutions in near real-time, and Pair-GRAPE-S more than satisfies the domain requirements, producing optimal solutions in near real-time. GRAPE-S and Pair-GRAPE-S are the first algorithms demonstrated to support near real-time coalition formation for very large, distributed collectives with multiple services

The $10 Million ANA Avatar XPRIZE Competition Advanced Immersive Telepresence Systems

The $10M ANA Avatar XPRIZE aimed to create avatar systems that can transport human presence to remote locations in real time. The participants of this multi-year competition developed robotic systems that allow operators to see, hear, and interact with a remote environment in a way that feels as if they are truly there. On the other hand, people in the remote environment were given the impression that the operator was present inside the avatar robot. At the competition finals, held in November 2022 in Long Beach, CA, USA, the avatar systems were evaluated on their support for remotely interacting with humans, exploring new environments, and employing specialized skills. This article describes the competition stages with tasks and evaluation procedures, reports the results, presents the winning teams' approaches, and discusses lessons learned.Comment: Extended version of article accepted for competitions colum