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- Member of: ASU Electronic Theses and Dissertations
Description
Mobile robots are used in a broad range of application areas; e.g. search and rescue, reconnaissance, exploration, etc. Given the increasing need for high performance mobile robots, the area has received attention by researchers. In this thesis, critical control and control-relevant design issues for differential drive mobile robots is addressed. Two major themes that have been explored are the use of kinematic models for control design and the use of decentralized proportional plus integral (PI) control. While these topics have received much attention, there still remain critical questions which have not been rigorously addressed. In this thesis, answers to the following critical questions are provided: When is 1. a kinematic model sufficient for control design? 2. coupled dynamics essential? 3. a decentralized PI inner loop velocity controller sufficient? 4. centralized multiple-input multiple-output (MIMO) control essential? and how can one design the robot to relax the requirements implied in 1 and 2? In this thesis, the following is shown: 1. The nonlinear kinematic model will suffice for control design when the inner velocity (dynamic) loop is much faster (10X) than the slower outer positioning loop. 2. A dynamic model is essential when the inner velocity (dynamic) loop is less than two times faster than the slower outer positioning loop. 3. A decentralized inner loop PI velocity controller will be sufficient for accomplish- ing high performance control when the required velocity bandwidth is small, rel- ative to the peak dynamic coupling frequency. A rule-of-thumb which depends on the robot aspect ratio is given. 4. A centralized MIMO velocity controller is needed when the required bandwidth is large, relative to the peak dynamic coupling frequency. Here, the analysis in the thesis is sparse making the topic an area for future analytical work. Despite this, it is clearly shown that a centralized MIMO inner loop controller can offer increased performance vis- ́a-vis a decentralized PI controller. 5. Finally, it is shown how the dynamic coupling depends on the robot aspect ratio and how the coupling can be significantly reduced. As such, this can be used to ease the requirements imposed by 2 and 4 above.
ContributorsAnvari, Iman (Author) / Rodriguez, Armando A (Thesis advisor) / Si, Jenni (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2013
Description
This thesis discusses control and obstacle avoidance for non-holonomic differential drive mobile vehicles. The two important behaviors for the vehicle can be defined as go to goal and obstacle avoidance behavior. This thesis discusses both behaviors in detail. Go to goal behavior is the ability of the mobile vehicle to go from one particular co-ordinate to another. Cruise control, cartesian and posture stabilization problems are discussed as the part of this behavior. Control strategies used for the above three problems are explained in the thesis. Matlab simulations are presented to verify these controllers. Obstacle avoidance behavior ensures that the vehicle doesn't hit object in its path while going towards the goal. Three different techniques for obstacle avoidance which are useful for different kind of obstacles are described in the thesis. Matlab simulations are presented to show and discuss the three techniques. The controls discussed for the cartesian and posture stabilization were implemented on a low cost miniature vehicle to verify the results practically. The vehicle is described in the thesis in detail. The practical results are compared with the simulations. Hardware and matlab codes have been provided as a reference for the reader.
ContributorsChopra, Dhruv (Author) / Rodriguez, Armando A (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2013
Description
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.
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
Description
Toward the ambitious long-term goal of a fleet of cooperating Flexible Autonomous Machines operating in an uncertain Environment (FAME), this thesis addresses various control objectives for ground vehicles.
There are two main objectives within this thesis, first is the use of visual information to control a Differential-Drive Thunder Tumbler (DDTT) mobile robot and second is the solution to a minimum time optimal control problem for the robot around a racetrack.
One method to do the first objective is by using the Position Based Visual Servoing (PBVS) approach in which a camera looks at a target and the position of the target with respect to the camera is estimated; once this is done the robot can drive towards a desired position (x_ref, z_ref). Another method is called Image Based Visual Servoing (IBVS), in which the pixel coordinates (u,v) of markers/dots placed on an object are driven towards the desired pixel coordinates (u_ref, v_ref) of the corresponding markers.
By doing this, the mobile robot gets closer to a desired pose (x_ref, z_ref, theta_ref).
For the second objective, a camera-based and noncamera-based (v,theta) cruise-control systems are used for the solution of the minimum time problem. To set up the minimum time problem, optimal control theory is used. Then a direct method is implemented by discretizing states and controls of the system. Finally, the solution is obtained by modeling the problem in AMPL and submitting to the nonlinear optimization solver KNITRO. Simulation and experimental results are presented.
The DDTT-vehicle used within this thesis has different components as summarized below:
(1) magnetic wheel-encoders/IMU for inner-loop speed-control and outer-loop directional control,
(2) Arduino Uno microcontroller-board for encoder-based inner-loop speed-control and encoder-IMU-based outer-loop cruise-directional-control,
(3) Arduino motor-shield for inner-loop speed-control,
(4) Raspberry Pi II computer-board for outer-loop vision-based cruise-position-directional-control,
(5) Raspberry Pi 5MP camera for outer-loop cruise-position-directional control.
Hardware demonstrations shown in this thesis are summarized: (1) PBVS without pan camera, (2) PBVS with pan camera, (3) IBVS with 1 marker/dot, (4) IBVS with 2 markers, (5) IBVS with 3 markers, (6) camera and (7) noncamera-based (v,theta) cruise control system for the minimum time problem.
There are two main objectives within this thesis, first is the use of visual information to control a Differential-Drive Thunder Tumbler (DDTT) mobile robot and second is the solution to a minimum time optimal control problem for the robot around a racetrack.
One method to do the first objective is by using the Position Based Visual Servoing (PBVS) approach in which a camera looks at a target and the position of the target with respect to the camera is estimated; once this is done the robot can drive towards a desired position (x_ref, z_ref). Another method is called Image Based Visual Servoing (IBVS), in which the pixel coordinates (u,v) of markers/dots placed on an object are driven towards the desired pixel coordinates (u_ref, v_ref) of the corresponding markers.
By doing this, the mobile robot gets closer to a desired pose (x_ref, z_ref, theta_ref).
For the second objective, a camera-based and noncamera-based (v,theta) cruise-control systems are used for the solution of the minimum time problem. To set up the minimum time problem, optimal control theory is used. Then a direct method is implemented by discretizing states and controls of the system. Finally, the solution is obtained by modeling the problem in AMPL and submitting to the nonlinear optimization solver KNITRO. Simulation and experimental results are presented.
The DDTT-vehicle used within this thesis has different components as summarized below:
(1) magnetic wheel-encoders/IMU for inner-loop speed-control and outer-loop directional control,
(2) Arduino Uno microcontroller-board for encoder-based inner-loop speed-control and encoder-IMU-based outer-loop cruise-directional-control,
(3) Arduino motor-shield for inner-loop speed-control,
(4) Raspberry Pi II computer-board for outer-loop vision-based cruise-position-directional-control,
(5) Raspberry Pi 5MP camera for outer-loop cruise-position-directional control.
Hardware demonstrations shown in this thesis are summarized: (1) PBVS without pan camera, (2) PBVS with pan camera, (3) IBVS with 1 marker/dot, (4) IBVS with 2 markers, (5) IBVS with 3 markers, (6) camera and (7) noncamera-based (v,theta) cruise control system for the minimum time problem.
ContributorsAldaco Lopez, Jesus (Author) / Rodriguez, Armando A. (Thesis advisor) / Artemiadis, Panagiotis K. (Committee member) / Berman, Spring M. (Committee member) / Arizona State University (Publisher)
Created2016
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 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.
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
Description
Swarms of low-cost, autonomous robots can potentially be used to collectively perform tasks over large domains and long time scales. The design of decentralized, scalable swarm control strategies will enable the development of robotic systems that can execute such tasks with a high degree of parallelism and redundancy, enabling effective operation even in the presence of unknown environmental factors and individual robot failures. Social insect colonies provide a rich source of inspiration for these types of control approaches, since they can perform complex collective tasks under a range of conditions. To validate swarm robotic control strategies, experimental testbeds with large numbers of robots are required; however, existing low-cost robots are specialized and can lack the necessary sensing, navigation, control, and manipulation capabilities.
To address these challenges, this thesis presents a formal approach to designing biologically-inspired swarm control strategies for spatially-confined coverage and payload transport tasks, as well as a novel low-cost, customizable robotic platform for testing swarm control approaches. Stochastic control strategies are developed that provably allocate a swarm of robots around the boundaries of multiple regions of interest or payloads to be transported. These strategies account for spatially-dependent effects on the robots' physical distribution and are largely robust to environmental variations. In addition, a control approach based on reinforcement learning is presented for collective payload towing that accommodates robots with heterogeneous maximum speeds. For both types of collective transport tasks, rigorous approaches are developed to identify and translate observed group retrieval behaviors in Novomessor cockerelli ants to swarm robotic control strategies. These strategies can replicate features of ant transport and inherit its properties of robustness to different environments and to varying team compositions. The approaches incorporate dynamical models of the swarm that are amenable to analysis and control techniques, and therefore provide theoretical guarantees on the system's performance. Implementation of these strategies on robotic swarms offers a way for biologists to test hypotheses about the individual-level mechanisms that drive collective behaviors. Finally, this thesis describes Pheeno, a new swarm robotic platform with a three degree-of-freedom manipulator arm, and describes its use in validating a variety of swarm control strategies.
To address these challenges, this thesis presents a formal approach to designing biologically-inspired swarm control strategies for spatially-confined coverage and payload transport tasks, as well as a novel low-cost, customizable robotic platform for testing swarm control approaches. Stochastic control strategies are developed that provably allocate a swarm of robots around the boundaries of multiple regions of interest or payloads to be transported. These strategies account for spatially-dependent effects on the robots' physical distribution and are largely robust to environmental variations. In addition, a control approach based on reinforcement learning is presented for collective payload towing that accommodates robots with heterogeneous maximum speeds. For both types of collective transport tasks, rigorous approaches are developed to identify and translate observed group retrieval behaviors in Novomessor cockerelli ants to swarm robotic control strategies. These strategies can replicate features of ant transport and inherit its properties of robustness to different environments and to varying team compositions. The approaches incorporate dynamical models of the swarm that are amenable to analysis and control techniques, and therefore provide theoretical guarantees on the system's performance. Implementation of these strategies on robotic swarms offers a way for biologists to test hypotheses about the individual-level mechanisms that drive collective behaviors. Finally, this thesis describes Pheeno, a new swarm robotic platform with a three degree-of-freedom manipulator arm, and describes its use in validating a variety of swarm control strategies.
ContributorsWilson, Sean Thomas (Author) / Berman, Spring M (Thesis advisor) / Artemiadis, Panagiotis (Committee member) / Sugar, Thomas (Committee member) / Rodriguez, Armando A (Committee member) / Taylor, Jesse (Committee member) / Arizona State University (Publisher)
Created2017
Description
Achieving human level intelligence is a long-term goal for many Artificial Intelligence (AI) researchers. Recent developments in combining deep learning and reinforcement learning helped us to move a step forward in achieving this goal. Reinforcement learning using a delayed reward mechanism is an approach to machine intelligence which studies decision making with control and how a decision making agent can learn to act optimally in an environment-unaware conditions.
Q-learning is one of the model-free reinforcement directed learning strategies which uses temporal differences to estimate the performances of state-action pairs called Q values. A simple implementation of Q-learning algorithm can be done using a Q table memory to store and update the Q values. However, with an increase in state space data due to a complex environment, and with an increase in possible number of actions an agent can perform, Q table reaches its space limit and would be difficult to scale well. Q-learning with neural networks eliminates the use of Q table by approximating the Q function using neural networks.
Autonomous agents need to develop cognitive properties and become self-adaptive to be deployable in any environment. Reinforcement learning with Q-learning have been very efficient in solving such problems. However, embedded systems like space rovers and autonomous robots rarely implement such techniques due to the constraints faced like processing power, chip area, convergence rate and cost of the chip. These problems present a need for a portable, low power, area efficient hardware accelerator to accelerate the process of such learning.
This problem is targeted by implementing a hardware schematic architecture for Q-learning using Artificial Neural networks. This architecture exploits the massive parallelism provided by neural network with a dedicated fine grain parallelism provided by a Field Programmable Gate Array (FPGA) thereby processing the Q values at a high throughput. Mars exploration rovers currently use Xilinx-Space-grade FPGA devices for image processing, pyrotechnic operation control and obstacle avoidance. The hardware resource consumption for the architecture has been synthesized considering Xilinx Virtex7 FPGA as the target device.
Q-learning is one of the model-free reinforcement directed learning strategies which uses temporal differences to estimate the performances of state-action pairs called Q values. A simple implementation of Q-learning algorithm can be done using a Q table memory to store and update the Q values. However, with an increase in state space data due to a complex environment, and with an increase in possible number of actions an agent can perform, Q table reaches its space limit and would be difficult to scale well. Q-learning with neural networks eliminates the use of Q table by approximating the Q function using neural networks.
Autonomous agents need to develop cognitive properties and become self-adaptive to be deployable in any environment. Reinforcement learning with Q-learning have been very efficient in solving such problems. However, embedded systems like space rovers and autonomous robots rarely implement such techniques due to the constraints faced like processing power, chip area, convergence rate and cost of the chip. These problems present a need for a portable, low power, area efficient hardware accelerator to accelerate the process of such learning.
This problem is targeted by implementing a hardware schematic architecture for Q-learning using Artificial Neural networks. This architecture exploits the massive parallelism provided by neural network with a dedicated fine grain parallelism provided by a Field Programmable Gate Array (FPGA) thereby processing the Q values at a high throughput. Mars exploration rovers currently use Xilinx-Space-grade FPGA devices for image processing, pyrotechnic operation control and obstacle avoidance. The hardware resource consumption for the architecture has been synthesized considering Xilinx Virtex7 FPGA as the target device.
ContributorsGankidi, Pranay Reddy (Author) / Thangavelautham, Jekanthan (Thesis advisor) / Ren, Fengbo (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2016
Description
Multi-segment manipulators and mobile robot collectives are examples of multi-agent robotic systems, in which each segment or robot can be considered an agent. Fundamental motion control problems for such systems include the stabilization of one or more agents to target configurations or trajectories while preventing inter-agent collisions, agent collisions with obstacles, and deadlocks. Despite extensive research on these control problems, there are still challenges in designing controllers that (1) are scalable with the number of agents; (2) have theoretical guarantees on collision-free agent navigation; and (3) can be used when the states of the agents and the environment are only partially observable. Existing centralized and distributed control architectures have limited scalability due to their computational complexity and communication requirements, while decentralized control architectures are often effective only under impractical assumptions that do not hold in real-world implementations. The main objective of this dissertation is to develop and evaluate decentralized approaches for multi-agent motion control that enable agents to use their onboard sensors and computational resources to decide how to move through their environment, with limited or absent inter-agent communication and external supervision. Specifically, control approaches are designed for multi-segment manipulators and mobile robot collectives to achieve position and pose (position and orientation) stabilization, trajectory tracking, and collision and deadlock avoidance. These control approaches are validated in both simulations and physical experiments to show that they can be implemented in real-time while remaining computationally tractable. First, kinematic controllers are proposed for position stabilization and trajectory tracking control of two- or three-dimensional hyper-redundant multi-segment manipulators. Next, robust and gradient-based feedback controllers are presented for individual holonomic and nonholonomic mobile robots that achieve position stabilization, trajectory tracking control, and obstacle avoidance. Then, nonlinear Model Predictive Control methods are developed for collision-free, deadlock-free pose stabilization and trajectory tracking control of multiple nonholonomic mobile robots in known and unknown environments with obstacles, both static and dynamic. Finally, a feedforward proportional-derivative controller is defined for collision-free velocity tracking of a moving ground target by multiple unmanned aerial vehicles.
ContributorsSalimi Lafmejani, Amir (Author) / Berman, Spring (Thesis advisor) / Tsakalis, Konstantinos (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2022
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 was to show how off-the-shelf (low-cost) remote-control (RC) “toy” vehicles can be converted into intelligent multi-capability robotic-platforms for conducting FAME research. This is shown for two vehicle classes: (1) six differential-drive (DD) RC vehicles called Thunder Tumbler (DDTT) and (2) one rear-wheel drive (RWD) RC car called Ford F-150 (1:14 scale). Each DDTT-vehicle was augmented to provide a substantive suite of capabilities as summarized below (It should be noted, however, that only one DDTT-vehicle was augmented with an inertial measurement unit (IMU) and 2.4 GHz RC capability): (1) magnetic wheel-encoders/IMU for(dead-reckoning-based) inner-loop speed-control and outer-loop position-directional-control, (2) Arduino Uno microcontroller-board for encoder-based inner-loop speed-control and encoder-IMU-ultrasound-based outer-loop cruise-position-directional-separation-control, (3) Arduino motor-shield for inner-loop motor-speed-control, (4)Raspberry Pi II computer-board for demanding outer-loop vision-based cruise- position-directional-control, (5) Raspberry Pi 5MP camera for outer-loop cruise-position-directional-control (exploiting WiFi to send video back to laptop), (6) forward-pointing ultrasonic distance/rangefinder sensor for outer-loop separation-control, and (7) 2.4 GHz spread-spectrum RC capability to replace original 27/49 MHz RC. Each “enhanced”/ augmented DDTT-vehicle costs less than 175 but offers the capability of commercially available vehicles costing over 500. Both the Arduino and Raspberry are low-cost, well-supported (software wise) and easy-to-use. For the vehicle classes considered (i.e. DD, RWD), both kinematic and dynamical (planar xy) models are examined. Suitable nonlinear/linear-models are used to develop inner/outer-loopcontrol laws.
All demonstrations presented involve enhanced DDTT-vehicles; one the F-150; one a quadrotor. The following summarizes key hardware demonstrations: (1) cruise-control along line, (2) position-control along line (3) position-control along curve (4) planar (xy) Cartesian stabilization, (5) cruise-control along jagged line/curve, (6) vehicle-target spacing-control, (7) multi-robot spacing-control along line/curve, (8) tracking slowly-moving remote-controlled quadrotor, (9) avoiding obstacle while moving toward target, (10) RC F-150 followed by DDTT-vehicle. Hardware data/video is compared with, and corroborated by, model-based simulations. In short, many capabilities that are critical for reaching the longer-term FAME goal are demonstrated.
critical modeling, design and control objectives for ground vehicles. One central objective was to show how off-the-shelf (low-cost) remote-control (RC) “toy” vehicles can be converted into intelligent multi-capability robotic-platforms for conducting FAME research. This is shown for two vehicle classes: (1) six differential-drive (DD) RC vehicles called Thunder Tumbler (DDTT) and (2) one rear-wheel drive (RWD) RC car called Ford F-150 (1:14 scale). Each DDTT-vehicle was augmented to provide a substantive suite of capabilities as summarized below (It should be noted, however, that only one DDTT-vehicle was augmented with an inertial measurement unit (IMU) and 2.4 GHz RC capability): (1) magnetic wheel-encoders/IMU for(dead-reckoning-based) inner-loop speed-control and outer-loop position-directional-control, (2) Arduino Uno microcontroller-board for encoder-based inner-loop speed-control and encoder-IMU-ultrasound-based outer-loop cruise-position-directional-separation-control, (3) Arduino motor-shield for inner-loop motor-speed-control, (4)Raspberry Pi II computer-board for demanding outer-loop vision-based cruise- position-directional-control, (5) Raspberry Pi 5MP camera for outer-loop cruise-position-directional-control (exploiting WiFi to send video back to laptop), (6) forward-pointing ultrasonic distance/rangefinder sensor for outer-loop separation-control, and (7) 2.4 GHz spread-spectrum RC capability to replace original 27/49 MHz RC. Each “enhanced”/ augmented DDTT-vehicle costs less than 175 but offers the capability of commercially available vehicles costing over 500. Both the Arduino and Raspberry are low-cost, well-supported (software wise) and easy-to-use. For the vehicle classes considered (i.e. DD, RWD), both kinematic and dynamical (planar xy) models are examined. Suitable nonlinear/linear-models are used to develop inner/outer-loopcontrol laws.
All demonstrations presented involve enhanced DDTT-vehicles; one the F-150; one a quadrotor. The following summarizes key hardware demonstrations: (1) cruise-control along line, (2) position-control along line (3) position-control along curve (4) planar (xy) Cartesian stabilization, (5) cruise-control along jagged line/curve, (6) vehicle-target spacing-control, (7) multi-robot spacing-control along line/curve, (8) tracking slowly-moving remote-controlled quadrotor, (9) avoiding obstacle while moving toward target, (10) RC F-150 followed by DDTT-vehicle. Hardware data/video is compared with, and corroborated by, model-based simulations. In short, many capabilities that are critical for reaching the longer-term FAME goal are demonstrated.
ContributorsLin, Zhenyu (Author) / Rodriguez, Armando Antonio (Committee member) / Si, Jennie (Committee member) / Berman, Spring Melody (Committee member) / Arizona State University (Publisher)
Created2015