24,575 research outputs found

    Reactive integrated motion planning and execution using Chekhov

    Get PDF
    Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 99-100).We envision a world in which robots and humans can collaborate to perform complex tasks in real-world environments. Current motion planners successfully generate trajectories for a robot with multiple degrees of freedom, in a cluttered environment, and ensure that the robot can achieve its goal while avoiding all the obstacles in the environment. However, these planners are not practical in real world scenarios that involve unstructured, dynamic environments for a three primary reasons. First, these motion planners assume that the environment the robot is functioning in, is well-known and static, both during plan generation and plan execution. Second, these planners do not support temporal constraints, which are crucial for planning in a rapidly-changing environment and for allowing task synchronisation between the robot and other agents, like a human or even another robot. Third, the current planners do not adequately represent the requirements of the task. They often over-constrain the task description and are hence unable to take advantage of task flexibility which may aid in optimising energy efficiency or robustness. In this thesis we present Chekhov, a reactive, integrated motion planning and execution executive that addresses these shortcomings using four key innovations. First, unlike traditional planners, the planning and execution components of Chekhov are very closely integrated. This close coupling blurs the traditional, sharp boundary between the two components and allows for optimal collaboration. Second, Chekhov represents temporal constraints, which allows it to perform operations that are temporally synchronised with external events. Third, Chekhov uses an incremental search algorithm which allows it to rapidly generate a new plan if a disturbance is encountered that threatens the execution of the existing plan. Finally, unlike standard planners which generate a single reference trajectory from the start pose to the goal pose, Chekhov generates a Qualitative Control Plan using Flow Tubes that represent families of feasible trajectories and associated control policies. These flow tubes provide Chekhov with a flexibility that is extremely valuable and serve as Chekhov's first line of defence.by Ameya Shroff.M. Eng

    Flexible human-robot cooperation models for assisted shop-floor tasks

    Get PDF
    The Industry 4.0 paradigm emphasizes the crucial benefits that collaborative robots, i.e., robots able to work alongside and together with humans, could bring to the whole production process. In this context, an enabling technology yet unreached is the design of flexible robots able to deal at all levels with humans' intrinsic variability, which is not only a necessary element for a comfortable working experience for the person but also a precious capability for efficiently dealing with unexpected events. In this paper, a sensing, representation, planning and control architecture for flexible human-robot cooperation, referred to as FlexHRC, is proposed. FlexHRC relies on wearable sensors for human action recognition, AND/OR graphs for the representation of and reasoning upon cooperation models, and a Task Priority framework to decouple action planning from robot motion planning and control.Comment: Submitted to Mechatronics (Elsevier
    corecore