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.
dc.creatorPlaku, Erion
dc.date.accessioned2018-12-03T18:31:45Z
dc.date.available2018-12-03T18:31:45Z
dc.date.issued2008
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.
dc.format.extent147 pp
dc.identifier.callnoTHESIS COMP. SCI. 2009 PLAKU
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>.
dc.identifier.digital304510255
dc.identifier.urihttps://hdl.handle.net/1911/103600
dc.language.isoeng
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.
dc.subjectRobotics
dc.subjectArtificial intelligence
dc.subjectComputer science
dc.subjectApplied sciences
dc.subjectAutonomous robots
dc.subjectDiscrete planning
dc.subjectDynamics
dc.subjectExploration
dc.subjectHigh-level tasks
dc.subjectHybrid systems
dc.subjectMotion planning
dc.titleFrom high-level tasks to low-level motions: Motion planning for high-dimensional nonlinear hybrid robotic systems
dc.typeThesis
dc.type.materialText
thesis.degree.departmentComputer Science
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
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