From high-level tasks to low-level motions: Motion planning for high-dimensional nonlinear hybrid robotic systems

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dc.contributor.advisorKavraki, Lydia E.en_US
dc.creatorPlaku, Erionen_US
dc.date.accessioned2018-12-03T18:31:45Zen_US
dc.date.available2018-12-03T18:31:45Zen_US
dc.date.issued2008en_US
dc.description.abstractA significant challenge of autonomous robotics in transportation, exploration, and search-and-rescue missions lies in the area of motion planning. The overall objective is to enable robots to automatically plan the low-level motions needed to accomplish assigned high-level tasks. Toward this goal, this thesis proposes a novel multi-layered approach, termed Synergic Combination of Layers of Planning ( SyCLoP ), that synergically combines high-level discrete planning and low-level motion planning. High-level discrete planning, which draws from research in AI and logic, guides low-level motion planning during the search for a solution. Information gathered during the search is in turn fed back from the low-level to the high-level layer in order to improve the high-level plan in the next iteration. In this way, high-level plans become increasingly useful in guiding the low-level motion planner toward a solution. This synergic combination of high-level discrete planning and low-level motion planning allows SyCLoP to solve motion-planning problems with respect to rich models of the robot and the physical world. This facilitates the design of feedback controllers that enable the robot to execute in the physical world solutions obtained in simulation. In particular, SyCLoP effectively solves challenging motion-planning problems that incorporate robot dynamics, physics-based simulations, and hybrid systems. Hybrid systems move beyond continuous models by employing discrete logic to instantaneously modify the underlying robot dynamics to respond to mishaps or unanticipated changes in the environment. Experiments in this thesis show that SyCLoP obtains significant computational speedup of one to two orders of magnitude when compared to state-of-the-art motion planners. In addition to planning motions that allow the robot to reach a desired destination while avoiding collisions, SyCLoP can take into account high-level tasks specified using the expressiveness of linear temporal logic (LTL). LTL allows for complex specifications, such as sequencing, coverage, and other combinations of temporal objectives. Going beyond motion planning, SyCLoP also provides a useful framework for discovering violations of safety properties in hybrid systems.en_US
dc.format.extent147 ppen_US
dc.identifier.callnoTHESIS COMP. SCI. 2009 PLAKUen_US
dc.identifier.citationPlaku, Erion. "From high-level tasks to low-level motions: Motion planning for high-dimensional nonlinear hybrid robotic systems." (2008) Diss., Rice University. <a href="https://hdl.handle.net/1911/103600">https://hdl.handle.net/1911/103600</a>.en_US
dc.identifier.digital304510255en_US
dc.identifier.urihttps://hdl.handle.net/1911/103600en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectRoboticsen_US
dc.subjectArtificial intelligenceen_US
dc.subjectComputer scienceen_US
dc.subjectApplied sciencesen_US
dc.subjectAutonomous robotsen_US
dc.subjectDiscrete planningen_US
dc.subjectDynamicsen_US
dc.subjectExplorationen_US
dc.subjectHigh-level tasksen_US
dc.subjectHybrid systemsen_US
dc.subjectMotion planningen_US
dc.titleFrom high-level tasks to low-level motions: Motion planning for high-dimensional nonlinear hybrid robotic systemsen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentComputer Scienceen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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