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To achieve the ambitious long-term goal of a feet of cooperating Flexible Autonomous

Machines operating in an uncertain Environment (FAME), this thesis addresses several

critical modeling, design, control objectives for rear-wheel drive ground vehicles.

Toward this ambitious goal, several critical objectives are addressed. One central objective of the thesis was to show how

To achieve the ambitious long-term goal of a feet of cooperating Flexible Autonomous

Machines operating in an uncertain Environment (FAME), this thesis addresses several

critical modeling, design, control objectives for rear-wheel drive ground vehicles.

Toward this ambitious goal, several critical objectives are addressed. One central objective of the thesis was to show how to build low-cost multi-capability robot platform

that can be used for conducting FAME research.

A TFC-KIT car chassis was augmented to provide a suite of substantive capabilities.

The augmented vehicle (FreeSLAM Robot) costs less than $500 but offers the capability

of commercially available vehicles costing over $2000.

All demonstrations presented involve rear-wheel drive FreeSLAM robot. The following

summarizes the key hardware demonstrations presented and analyzed:

(1)Cruise (v, ) control along a line,

(2) Cruise (v, ) control along a curve,

(3) Planar (x, y) Cartesian Stabilization for rear wheel drive vehicle,

(4) Finish the track with camera pan tilt structure in minimum time,

(5) Finish the track without camera pan tilt structure in minimum time,

(6) Vision based tracking performance with different cruise speed vx,

(7) Vision based tracking performance with different camera fixed look-ahead distance L,

(8) Vision based tracking performance with different delay Td from vision subsystem,

(9) Manually remote controlled robot to perform indoor SLAM,

(10) Autonomously line guided robot to perform indoor SLAM.

For most cases, hardware data is compared with, and corroborated by, model based

simulation data. In short, the thesis uses low-cost self-designed rear-wheel

drive robot to demonstrate many capabilities that are critical in order to reach the

longer-term FAME goal.
ContributorsLu, Xianglong (Author) / Rodriguez, Armando Antonio (Thesis advisor) / Berman, Spring (Committee member) / Artemiadis, Panagiotis (Committee member) / Arizona State University (Publisher)
Created2016
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Description
Robotic systems are outmatched by the abilities of the human hand to perceive and manipulate the world. Human hands are able to physically interact with the world to perceive, learn, and act to accomplish tasks. Limitations of robotic systems to interact with and manipulate the world diminish their usefulness. In

Robotic systems are outmatched by the abilities of the human hand to perceive and manipulate the world. Human hands are able to physically interact with the world to perceive, learn, and act to accomplish tasks. Limitations of robotic systems to interact with and manipulate the world diminish their usefulness. In order to advance robot end effectors, specifically artificial hands, rich multimodal tactile sensing is needed. In this work, a multi-articulating, anthropomorphic robot testbed was developed for investigating tactile sensory stimuli during finger-object interactions. The artificial finger is controlled by a tendon-driven remote actuation system that allows for modular control of any tendon-driven end effector and capabilities for both speed and strength. The artificial proprioception system enables direct measurement of joint angles and tendon tensions while temperature, vibration, and skin deformation are provided by a multimodal tactile sensor. Next, attention was focused on real-time artificial perception for decision-making. A robotic system needs to perceive its environment in order to make decisions. Specific actions such as “exploratory procedures” can be employed to classify and characterize object features. Prior work on offline perception was extended to develop an anytime predictive model that returns the probability of having touched a specific feature of an object based on minimally processed sensor data. Developing models for anytime classification of features facilitates real-time action-perception loops. Finally, by combining real-time action-perception with reinforcement learning, a policy was learned to complete a functional contour-following task: closing a deformable ziplock bag. The approach relies only on proprioceptive and localized tactile data. A Contextual Multi-Armed Bandit (C-MAB) reinforcement learning algorithm was implemented to maximize cumulative rewards within a finite time period by balancing exploration versus exploitation of the action space. Performance of the C-MAB learner was compared to a benchmark Q-learner that eventually returns the optimal policy. To assess robustness and generalizability, the learned policy was tested on variations of the original contour-following task. The work presented contributes to the full range of tools necessary to advance the abilities of artificial hands with respect to dexterity, perception, decision-making, and learning.
ContributorsHellman, Randall Blake (Author) / Santos, Veronica J (Thesis advisor) / Artemiadis, Panagiotis K (Committee member) / Berman, Spring (Committee member) / Helms Tillery, Stephen I (Committee member) / Fainekos, Georgios (Committee member) / Arizona State University (Publisher)
Created2016
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Description
Toward the ambitious long-term goal of a fleet of cooperating Flexible Autonomous Machines operating in an uncertain Environment (FAME), this thesis addresses several critical modeling, design and control objectives for ground vehicles. One central objective is formation of multi-robot systems, particularly, longitudinal control of platoon of ground vehicle. In this

Toward the ambitious long-term goal of a fleet of cooperating Flexible Autonomous Machines operating in an uncertain Environment (FAME), this thesis addresses several critical modeling, design and control objectives for ground vehicles. One central objective is formation of multi-robot systems, particularly, longitudinal control of platoon of ground vehicle. In this thesis, the author use low-cost ground robot platform shows that with leader information, the platoon controller can have better performance than one without it.

Based on measurement from multiple vehicles, motor-wheel system dynamic model considering gearbox transmission has been developed. Noticing the difference between on ground vehicle behavior and off-ground vehicle behavior, on ground vehicle-motor model considering friction and battery internal resistance has been put forward and experimentally validated by multiple same type of vehicles. Then simplified longitudinal platoon model based on on-ground test were used as basis for platoon controller design.

Hardware and software has been updated to facilitate the goal of control a platoon of ground vehicles. Based on previous work of Lin on low-cost differential-drive

(DD) RC vehicles called Thunder Tumbler, new robot platform named Enhanced

Thunder Tumbler (ETT 2) has been developed with following improvement: (1) optical wheel-encoder which has 2.5 times higher resolution than magnetic based one,

(2) BNO055 IMU can read out orientation directly that LSM9DS0 IMU could not,

(3) TL-WN722N Wifi USB Adapter with external antenna which can support more stable communication compared to Edimax adapter, (4) duplex serial communication between Pi and Arduino than single direction communication from Pi to Arduino, (5) inter-vehicle communication based on UDP protocol.

All demonstrations presented using ETT vehicles. The following summarizes key hardware demonstrations: (1) cruise-control along line, (2) longitudinal platoon control based on local information (ultrasonic sensor) without inter-vehicle communication, (3) longitudinal platoon control based on local information (ultrasonic sensor) and leader information (speed). Hardware data/video is compared with, and corroborated by, model-based simulations. Platoon simulation and hardware data reveals that with necessary information from platoon leader, the control effort will be reduced and space deviation be diminished among propagation along the fleet of vehicles. In short, many capabilities that are critical for reaching the longer-term FAME goal are demonstrated.
ContributorsLi, Zhichao (Author) / Rodriguez, Armando A (Thesis advisor) / Artemiadis, Panagiotis K (Committee member) / Berman, Spring M (Committee member) / Arizona State University (Publisher)
Created2016