In this thesis we investigate the problem of motion planning under environment uncertainty. Specifically, we focus on Task-Motion Planning (TMP) and probabilistic collision avoidance which are presented as two parts in this thesis. Though the two parts are largely self-contained, collision avoidance is an integral part of TMP or any robot motion planning problem in general. The problem of TMP which is the subject of Part I is by itself challenging and hence in Part I, collision computation is not the main focus and is addressed with a deterministic approach. Moreover, motion planning is performed offline since we assume static obstacles in the environment. Online TMP, incorporating dynamic obstacles or other environment changes is rather difficult due to the computational challenges associated with updating the changing task domain. As such, we devote Part II entirely to the field of online probabilistic collision avoidance motion planning. Of late, TMP for manipulation has attracted significant interest resulting in a proliferation of different approaches. In contrast, TMP for navigation has received considerably less attention. Autonomous robots operating in real-world complex scenarios require planning in the discrete (task) space and the continuous (motion) space. In knowledge-intensive domains, on the one hand, a robot has to reason at the highest-level, for example, the objects to procure, the regions to navigate to in order to acquire them; on the other hand, the feasibility of the respective navigation tasks have to be checked at the execution level. This presents a need for motion-planning-aware task planners. In Part I of this thesis, we discuss a probabilistically complete approach that leverages this task-motion interaction for navigating in large knowledge-intensive domains, returning a plan that is optimal at the task-level. The framework is intended for motion planning under motion and sensing uncertainty, which is formally known as Belief Space Planning (BSP). The underlying methodology is ...


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    Title :

    Multimodal Planning under Uncertainty: Task-Motion Planning and Collision Avoidance



    Publication date :

    2021-07-19


    Remarks:

    doi:10.15167/thomas-antony_phd2021-07-19



    Type of media :

    Theses


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    004 / 629



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