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Browsing by Subject "Motion Planning"

Now showing 1 - 3 of 3
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    A Formal Investigation of Human Spatial Control Skills: Mathematical Formalization, Skill Development, and Skill Assessment
    (2016-07) Li, Bin
    Spatial control behaviors account for a large proportion of human everyday activities from normal daily tasks, such as reaching for objects, to specialized tasks, such as driving, surgery, or operating equipment. These behaviors involve intensive interactions within internal processes (i.e. cognitive, perceptual, and motor control) and with the physical world. This dissertation builds on a concept of interaction pattern and a hierarchical functional model. Interaction pattern represents a type of behavior synergy that humans coordinates cognitive, perceptual, and motor control processes. It contributes to the construction of the hierarchical functional model that delineates humans spatial control behaviors as the coordination of three functional subsystems: planning, guidance, and tracking/pursuit. This dissertation formalizes and validates these two theories and extends them for the investigation of human spatial control skills encompassing development and assessment. Specifically, this dissertation first presents an overview of studies in human spatial control skills encompassing definition, characteristic, development, and assessment, to provide theoretical evidence for the concept of interaction pattern and the hierarchical functional model. The following, the human experiments for collecting motion and gaze data and techniques to register and classify gaze data, are described. This dissertation then elaborates and mathematically formalizes the hierarchical functional model and the concept of interaction pattern. These theories then enables the construction of a succinct simulation model that can reproduce a variety of human performance with a minimal set of hypotheses. This validates the hierarchical functional model as a normative framework for interpreting human spatial control behaviors. The dissertation then investigates human skill development and captures the emergence of interaction pattern. The final part of the dissertation applies the hierarchical functional model for skill assessment and introduces techniques to capture interaction patterns both from the top down using their geometric features and from the bottom up using their dynamical characteristics. The validity and generality of the skill assessment is illustrated using two the remote-control flight and laparoscopic surgical training experiments.
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    Integration of environment sensing and control functions for Robust Rotorcraft UAV (RUAV) guidance.
    (2012-05) Tehrani, Navid Dadkhah
    Unmanned Air Vehicles (UAVs) have started supplanting manned aircraft in a broad range of tasks. Vehicles such as miniature rotorcrafts with broad maneuvering range and small size can enter remote locations that are hard to reach using other air and ground vehicles. Developing a guidance system which enables a Rotorcraft UAV (RUAV) to perform such tasks involves combing key elements from robotics motion planning, control system design, trajectory optimization as well as dynamics modeling. The focus of this thesis is to integrate a guidance system for a small-scale rotorcraft to enable a high level of performance and situational awareness. We cover large aspects of the system integration including modeling, control system design, environment sensing as well as motion planning in the presence of uncertainty. The system integration in this thesis is performed around a Blade-CX2 miniature coaxial helicopter. The first part of the thesis focuses on the development of the parameterized model for the Blade-CX2 helicopter with an emphasis on the coaxial rotor configuration. The model explicitly accounts for the dynamics of lower rotor and uses an implicit lumped parameter model for the upper rotor and stabilizer-bar. The parameterized model was identified using frequency domain system identification. In the second part of the thesis, we use the identified model to design a control law for the Blade-CX2 helicopter. The control augmentation for the Blade-CX2 helicopter was based on a nested attitude-velocity loop control architecture and was designed following classical loop-shaping and dynamic inversion techniques. A path following layer wrapped around the velocity control system enables the rotorcraft to follow reference trajectories specified by a sequence of waypoints and velocity vectors. Such reference paths are common in autonomous guidance systems. Finally, the third part of the thesis addresses the problem of autonomous navigation through a partially known or unknown 3D cluttered environment. The proposed multi-layer hierarchical guidance framework is based on optimal control principles and relies on the interaction of several subsystems such as environment sensing and mapping, Cost-to-Go (CTG) function update, reactive planning and Receding Horizon (RH) optimization. It is also tightly integrated with the path following controller.
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    Learning To Communicate for Coordinated Multi-Agent Navigation
    (2019-06) Hildreth, Dalton James
    This work presents a decentralized multi-agent navigation approach that allows agents to coordinate their motion through local communication. Our approach allows agents to develop their own emergent language of communication through an optimization process that simultaneously determines what agents say in response to their spatial observations and how agents interpret communication from others to update their motion. We apply our communication approach together with the TTC-Forces crowd simulation algorithm and show a significant decrease in congestion and bottle-necking of agents, especially in scenarios where agents benefit from close coordination. In addition to reaching their goals faster, agents using our approach show coordinated behaviors including greeting, flocking, following, and grouping.Furthermore, we observe that communication strategies optimized for one scenario often continue to provide time-efficient, coordinated motion between agents when applied to different scenarios.This suggests that the agents are learning to generalize strategies for coordination through their communication “language".