Efficient Motion Planning for Wheeled Mobile Robotics

Bevilacqua, Paolo (2019) Efficient Motion Planning for Wheeled Mobile Robotics. PhD thesis, University of Trento.

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Nowadays, the field of wheeled robotics is undergoing an impressive growth and development. Different hardware and software components are being developed and applied in various contexts, including assistive robotics, industrial robotics, automotive, ... Motion Planning is a fundamental aspect for the development of autonomous wheeled mobile robots. The capability of planning safe, smooth trajectories, and to locally adjust them in real-time to deal with contingent situations and avoid collisions is an essential requirement to allow robots to work and perform activities in public spaces shared with humans. Moreover, in general, efficiency is a key constraint for this kind of applications, given the limited computational power usually available on robotic platforms. In this thesis, we focus on the development of efficient algorithms to solve different kind of motion planning problems. Specifically, in the first part of the thesis, we propose a complete planning system for an assisitive robot supporting the navigation of older users. The developed planner generates paths connecting different locations on the map, that are smooth and specifically tailored to optimize the comfort perceived by the human users. During the navigation, the system applies an efficient model to predict the behaviours of the surrounding pedestrians, and to locally adapt the reference path to minimise the probability of collisions. Finally, the motion planner is integrated with an "high-level" reasoning component, to generate and propose complete activities, like the visit to a museum or a shopping mall, specifically tailored to the preferences, needs and requirements of each user. In the second part of the thesis, we show how the efficient solutions and building blocks developed for the assistive robots, can be adapted and applied also to a completely different context, such as the generation of optimal trajectories for an autonomous racing vehicle.

Item Type:Doctoral Thesis (PhD)
Doctoral School:Information and Communication Technology
PhD Cycle:31
Subjects:Area 01 - Scienze matematiche e informatiche > INF/01 INFORMATICA
Area 09 - Ingegneria industriale e dell'informazione > ING-INF/05 SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI
Repository Staff approval on:19 Jul 2019 10:11

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