Lifelong information-driven exploration for mobile robots to complete and Refine spatio-temporal maps in changing environments
Creators: name: Santos, Joao
Publisher: name: University of Lincoln
Year: 2023
Subjects: H671 - Robotics

description: Recent improvements in the ability of mobile robots to operate safely in human populatedenvironments have allowed their deployment in households, offices and public buildings,such as museums and hospitals. However, the structure of these environments is typicallynot known a priori, which requires the robots to build their own models of their operationalenvironments. This process is commonly known as exploration in mobile robotics.Moreover, real-world environments tend to change over time, which means that to achievelong-term autonomous operation, robots must also update their environment models as apart of their daily routine. The assumption of a perpetually-changing world adds a temporaldimension to the exploration problem, making exploration a never-ending lifelonglearning process. To the best of our knowledge, this process termed lifelong explorationhas never been studied in detail before and forms the main topic of the work presented inthis thesis. Effcient lifelong exploration requires a robot to choose the right locations andtimes at which to collect observations in order to improve its environment model.To evaluate the ability of a robot to build and maintain its environment models, weneed to be able to compare lifelong exploration strategies under repeatable experimentalconditions. An evaluation methodology based on pre-recorded sensory datasets would notbe suitable for this purpose, as this would not allow the robot to choose the location or timeof its observations. Evaluating lifelong exploration requires the deterministic reproductionof environment changes, while preserving the robots ability to decide upon its own actionsduring the experiment. This thesis therefore contributes a new benchmarking methodologyfor lifelong exploration, which replicates the events occurring in real environments throughphysical simulations that reflect the environment changes gathered by ambient sensors overlong periods of time. The established experimental benchmarks are based on long-termsensory datasets recorded in a smart home, with dynamics produced by a single personover a period of one year, and an office environment, with dynamics produced by a teamof workers.Building upon the aforementioned benchmarking methodology, the thesis investigatespossible strategies for lifelong exploration. An experimental comparison of lifelong explorationstrategies that combine various decision-making paradigms and spatio-temporalrepresentations is presented. Moreover, a new approach to lifelong explorations is proposedthat applies information-theoretic exploration techniques to environment representationsthat model the uncertainty of world states as probabilistic functions of time. The proposedmethod explicitly models the world dynamics and can predict the environment changes.The predictive ability is used to reason about the most informative locations to explorefor a given time. A 16 week long experiment shows that the combination of dynamicenvironment representations with information-gain exploration principles allows to createand maintain up-to-date models of continuously changing environments, enabling efficientand self-improving long-term operation of mobile service robots.The final part of the thesis considers the problem of acquiring and maintaining dense3D models of dynamic environments during long-term operation, building on the workpresented in the earlier chapters. The term 4D mapping is used to indicate 3D mappingby mobile robots over extended periods of time. A new approach to lifelong 4D mappingand exploration is presented, which was deployed on a real robotic platform during long termoperation in real-world human-populated environments. The approach integratessensory data captured by the robot at different times and locations into a global, metricI 4D spatio-temporal model and then uses the model to decide where and when to performthe next round of observations. Finally, the deployment of the 4D exploration method in areal-world office scenario is described and evaluated. The one week long experiments showthat the method enables reliable 4D mapping and persistent self-localisation of autonomousmobile robots, continually improving the robots maps to reflect the ever-changing world.