Situation Assessment for Mobile Robots

Abstract

Mobile robotter er blevet en moden teknologi. De første kabelførte logistikroboter blev introduceret i industrien for næsten 60 år siden. Igennem denne tid, har markedet for mobile robotter i industrien kun bevidnet en meget moderat vækst og blot 2.100 systemer blev solgt verden over i 2011. I de senere år har mange andre domæner adopteret de mobile robotter, som f.eks. logistikrobotter på hospitaler og støvsuger-robotter i vores hjem. Men hvis man betragter de sidste 15 års forskningsresultater inden for perception og operation i naturlige omgivelse samt det store fald i prisen på sensor systemer, så er vækstoptientialet inden for mobile robotter enormt.Mange nye teknologi-komponenter er allerede klar til at flytte grænserne for anvendelsen af mobile robotter, men en nøgleudfordring er pålidelighed. Naturlige omgivelser er komplekse og dynamiske, hvor chancen for at robotten misfortolker omgivelserne eller forfejler detektionen af kritiske omstændigheder er ungåelige. For at håndtere disse udfordringer, må styringen af robot-applikationen være i stand til at håndtere fejlbehæftede observationer og komme videre efter uundgåelige fejl. Controlleren må vide hvad der foregår?Denne afhandling adresserer netop dette problem, ud fra den hypotese at vurdering af robottens situation, kan bidrage med essentiel viden til robotstyringen og gøre denne i stand til at forstå den robottens nuværende situation samt forudse den fremtidige status. Forudsigelse af situationer er præsenteret ved brug af on-line grafsøgning af maximum likelihood i EMM modellen.Et nyt framework for situations modellering bliver præsenteret, der anvender en Extensible Markov Model til at representere den spatio-temporale natur af situationer. On-line data-streams fra robottens sensorer og algorithmer bliver behandlet ved brug af stream-baseret clustering til at bygge den spatio-temporale structure og til at matche robottens situation til eksisterende tilstande.De udviklede software moduler er integreret i en ny software arkitektur, som muliggør integration imod et hvilkårligt robotstyrings framework og anvender on-line visualizering af den spatio-temporale graf-struktur for at optimere klassifikation af situationer.Resultaterne er evalueret i tre real-world scenarier der, med success, vurderer det præsenterede situation assessment framework i forhold til detektion af kendte spatio-temporale relationer, afvigelser fra kendte spatio-temporale mønstre og detektion af kendte kritiske situationer.Mobile robots have become a mature technology. The first cable guided logistics robots were introduced in the industry almost 60 years ago. In this time the market for mobile robots in industry has only experienced a very modest growth and only 2.100 systems were sold worldwide in 2011. In recent years, many other domains have adopted the mobile robots, such as logistics robots at hospitals and the vacuum robots in our homes. However, considering the achievements in research the last 15 years within perception and operation in natural environments together with the reductions of costs in modern sensor systems, the growth potential for mobile robot applications are enormous.Many new technological components are available to move the limits of commercial mobile robot applications, but a key hindrance is reliability. Natural environments are complex and dynamic, and thus the risk of robots misinterpreting the environment or failing to detect critical circumstances is unavoidable. To deal with this challenge, the control of robot applications must be able to handle imperfect observations and gracefully recover from unavoidable errors. The controller needs to know what is going on.This thesis addresses exactly this problem from the hypothesis that an assessment of the situation for the robot, will be able to contribute with essential knowledge to the robot control and enable the understanding of the current situation as well as predict the future status.A novel framework for situation modeling are presented, which applies an Extensible Markov Model (EMM) to represent the spatio-temporal nature of situations. On-line data-streams from the robot sensors and algorithms are processed using stream-based clustering to build the spatio-temporal structure or match the situation of the robot to existing states. Situation prediction is proposed using an on-line graph-search of maximum likelihoods in the EMM.The developed software modules are integrated in a new software architecture, which facilitates integration into any robotic control framework and uses on-line visualization of the spatio-temporal graphs to optimize situation classification.The results are evaluated in three real-world scenarios, which successfully evaluates capabilities of the proposed situation assessment framework within detection of known spatio-temporal relations, deviation from known spatio-temporal patterns, and detection of known critical situations