Socially aware motion planning of assistive robots in crowded environments

Colombo, Alessio (2015) Socially aware motion planning of assistive robots in crowded environments. PhD thesis, University of Trento.

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People with impaired physical or mental ability often find it challenging to negotiate crowded or unfamiliar environments, leading to a vicious cycle of deteriorating mobility and sociability. In particular, crowded environments pose a challenge to the comfort and safety of those people. To address this issue we present a novel two-level motion planning framework to be embedded efficiently in portable devices. At the top level, the long term planner deals with crowded areas, permanent or temporary anomalies in the environment (e.g., road blocks, wet floors), and hard and soft constraints (e.g., "keep a toilet within reach of 10 meters during the journey", "always avoid stairs"). A priority tailored on the user's needs can also be assigned to the constraints. At the bottom level, the short term planner anticipates undesirable circumstances in real time, by verifying simulation traces of local crowd dynamics against temporal logical formulae. The model takes into account the objectives of the user, preexisting knowledge of the environment and real time sensor data. The algorithm is thus able to suggest a course of action to achieve the user’s changing goals, while minimising the probability of problems for the user and other people in the environment. An accurate model of human behaviour is crucial when planning motion of a robotic platform in human environments. The Social Force Model (SFM) is such a model, having parameters that control both deterministic and stochastic elements. The short term planner embeds the SFM in a control loop that determines higher level objectives and reacts to environmental changes. Low level predictive modelling is provided by the SFM fed by sensors; high level logic is provided by statistical model checking. To parametrise and improve the short term planner, we have conducted experiments to consider typical human interactions in crowded environments. We have identified a number of behavioural patterns which may be explicitly incorporated in the SFM to enhance its predictive power. To validate our hierarchical motion planner we have run simulations and experiments with elderly people within the context of the DALi European project. The performance of our implementation demonstrates that our technology can be successfully embedded in a portable device or robot.

Item Type:Doctoral Thesis (PhD)
Doctoral School:Information and Communication Technology
PhD Cycle:26
Subjects:Area 01 - Scienze matematiche e informatiche > INF/01 INFORMATICA
Area 11 - Scienze storiche, filosofiche, pedagogiche e psicologiche > M-PSI/05 PSICOLOGIA SOCIALE
Area 01 - Scienze matematiche e informatiche > MAT/06 PROBABILITÀ E STATISTICA MATEMATICA
Repository Staff approval on:14 May 2015 12:07

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