Search Results (1 - 7 of 7 Results)

Sort By  
Sort Dir
 
Results per page  

Liu, YipingFuzzy Control of Hopping in a Biped Robot
Master of Science, The Ohio State University, 2010, Electrical and Computer Engineering

Current bipedal robots with articulated legs, even the most impressive prototypes to date, still lack the ability to execute dynamic motions such as jumping and running with comparable performance to biological systems. Recently a new biped prototype, KURMET, has been built at OSU to serve as an experimental platform for further investigation into the performance of dynamic movements in bipedal machines with biologically-realistic features. KURMET has series-elastic actuators (SEA) at all leg joints. The presence of SEAs provides the compliance that is needed in dynamic motions, yet also complicates the controller's tasks, especially when combined with articulated legs in a system that is not naturally stable.

This thesis develops a fuzzy control system for hopping with KURMET. With this controller, KURMET can stably hop at varying heights and forward/backward velocities. The control system is arranged into two levels. The low-level control executes the hop motion. It employs a hopping state machine that is specifically designed to accommodate the natural dynamics of the SEAs. The high-level control is a fuzzy controller that is called at discrete instances (every top of flight (TOF)) to regulate the key parameters in the state machine. Through proper selection of these parameters, the desired hop height and velocity can be achieved. The fuzzy rulebase is generated via an iterative training process, which is done off-line through dynamic simulation using detailed models of the articulated mechanism and the series-elastic actuation. The fuzzy rulebase is later modified by on-line adaptation.

The fuzzy rulebase has fewer than 200 rules; however, the overall fuzzy control system is able to produce robust and accurate hopping performance in KURMET. Experimental data shows that the maximum error of the torso height at TOF is controlled within 1 cm and the maximum error of the torso velocity at TOF is controlled within 5 cm/s.

This thesis also experimentally investigates the high jump potential in KURMET. With a jumping state machine that is modified from the previous hopping state machine, KURMET is able to produce a maximum nominal jump height of 75 cm. When normalized to the length of the biped's link segments (25 cm), this performance is significant relative to human jumps.

Committee:

David Orin (Advisor); Yuan F. Zheng (Other)

Subjects:

Electrical Engineering; Engineering; Mechanical Engineering; Robots

Keywords:

biped;robot;KURMET;legged;locomotion;dynamic movement;jump;hop;robotics;fuzzy control;intelligent control;SEA;series-elastic actuator;state machine

Pavlic, Theodore P.Optimal Foraging Theory Revisited
Master of Science, The Ohio State University, 2007, Electrical Engineering
Optimal foraging theory explains adaptation via natural selection through quantitative models. Behaviors that are most likely to be favored by natural selection can be predicted by maximizing functions representing Darwinian fitness. Optimization has natural applications in engineering, and so this approach can also be used to design behaviors of engineered agents. In this thesis, we generalize ideas from optimal foraging theory to allow for its easy application to engineering design. By extending standard models and suggesting new value functions of interest, we enhance the analytical efficacy of optimal foraging theory and suggest possible optimality reasons for previously unexplained behaviors observed in nature. Finally, we develop a procedure for maximizing a class of optimization functions relevant to our general model. As designing strategies to maximize returns in a stochastic environment is effectively an optimal portfolio problem, our methods are influenced by results from modern and post-modern portfolio theory. We suggest that optimal foraging theory could benefit by injecting updated concepts from these economic areas.

Committee:

Kevin Passino (Advisor)

Keywords:

robotics; automation; autonomous vehicles; behavior; behavioral ecology; intelligent control; portfolio theory; modern portfolio theory; MPT; post-modern portfolio theory; PMPT; optimal foraging theory; OFT; optimal diet selection; predator; prey

Rajasingh, JoshuaLane Detection and Obstacle Avoidance in Mobile Robots
MS, University of Cincinnati, 2010, Engineering and Applied Science: Mechanical Engineering

Robots are getting more and more involved in every facet of life. They have replaced humans in many fields with their dependability and adaptability. While they have been around for more than half a century, only recently have they developed the intelligence to sense their surroundings and behave accordingly. This thesis describes an approach to navigating a robot along a roadway-type course bound with white lines and strewn with obstacles.

The Bearcat Cub is the University of Cincinnati’s mobile robot developed for competing at the Intelligent Ground Vehicle Competition (IGVC). The Autonomous Challenge component of the IGVC requires the robot to navigate through an unknown complex obstacle course. The robot must sense its surroundings, detect its confines and calculate a heading. The Bearcat Cub utilizes vision and laser sensor reading to detect the lanes and obstacles respectively.

This thesis describes how the robot processes the camera images, detects the lanes and calculates a heading. It also discusses how obstacles are detected and suitable avoidance methods. Finally, a new approach to combining both systems has been described. This avoids conflict in the results from both systems. Various lighting conditions and obstacle layouts have been tried and tested to ensure the algorithms effectiveness.

The vision processing algorithm discussed in this thesis was tested in the 2008 Intelligent Ground Vehicle Competition with acceptable outcomes. The obstacle avoidance methods have been developed later and can be implemented in future competitions.

Committee:

Ernest Hall, PhD (Committee Chair); Ronald Huston, PhD (Committee Member); Manish Kumar, PhD (Committee Member)

Subjects:

Robots

Keywords:

Autonomous Mobile Robot;Lane Detection;Obstacle Avoidance;Intelligent Control System;Path Planning;Image Processing

Wilmot, Timothy AllenIntelligent Controls for a Semi-Active Hydraulic Prosthetic Knee
Master of Science in Electrical Engineering, Cleveland State University, 2011, Fenn College of Engineering
We discuss open loop control development and simulation results for a semi-active above-knee prosthesis. The control signal consists of two hydraulic valve settings. These valves control a rotary actuator that provides torque to the prosthetic knee. We develop open loop control using biogeography-based optimization (BBO), which is a recently developed evolutionary algorithm, and gradient descent. We use gradient descent to show that the control generated by BBO is locally optimal. This research contributes to the field of evolutionary algorithms by demonstrating that BBO is successful at finding optimal solutions to complex, real-world, nonlinear, time varying control problems. The research contributes to the field of prosthetics by showing that it is possible to find effective open loop control signals for a newly proposed semi-active hydraulic knee prosthesis. The control algorithm provides knee angle tracking with an RMS error of 7.9 degrees, and thigh angle tracking with an RMS error of 4.7 degrees. Robustness tests show that the BBO control solution is affected very little by disturbances added during the simulation. However, the open loop control is very sensitive to the initial conditions. So a closed loop control is needed to mitigate the effects of varying initial conditions. We implement a proportional, integral, derivative (PID) controller for the prosthesis and show that it is not a sufficient form of closed loop control. Instead, we implement artificial neural networks (ANNs) as the mechanism for closed loop control. We show that ANNs can greatly improve performance when noise and disturbance cause high tracking errors, thus reducing the risk of stumbles and falls. We also show that ANNs are able to improve average performance by as much as 8% over open loop control. We also discuss embedded system implementation with a microcontroller and associated hardware and software.

Committee:

Dan Simon, PhD (Advisor); Fuquin Xiong, PhD (Committee Member); Lili Dong, PhD (Committee Member)

Subjects:

Electrical Engineering; Engineering

Keywords:

prosthetic control; ANN; artificial neural network; BBO; biogeography based optimization; intelligent control; nonlinear control problem; time varying control problem; evolutionary algorithm; gradient descent

Zhang, ZhanDeveloping a Unified Perspective on the Role of Multiresolution in Machine Intelligence Tasks
Doctor of Philosophy, Case Western Reserve University, 2005, Electrical Engineering
MultiResolution Analysis (MRA) is a common phenomenon of human intelligence. The basic procedure of MRA is that a series of analyses are carried out on an object's representations at different, progressively increasing resolution levels and analysis results at lower resolution levels act as guidance to analyses at higher resolution levels. Many methods such as coarse-fine template matching, reduced model in optimization, coarse-fine path-finding and so on can be seen as implementations of principles of MRA. This dissertation reports on investigations of MRA in different areas from a unified perspective and proposes algorithms from the viewpoint of MRA for attacking machine intelligence tasks in areas such as classification, function approximation, rule learning, optimization by stochastic search, control, and so on. The focus of this dissertation is about three questions: firstly,what is multiresolution? secondly, how to obtain multiresolution respresentations of an object? and thirdly, how to utilize results attained at low resolution levels as guidance for analyses at high resolution levels? Two new concepts, resolution and scale of a cluster, are introduced in this dissertation, and based on these concepts clustering algorithms are developed for obtaining multiresolution representations of an object. Using this uniform approach to attaining multiresolution representations, implementations of MRA are discussed and illustrated with examples for various machine intelligence tasks.

Committee:

Yoh-Han Pao (Advisor)

Keywords:

Clustering; Rule learning; Optimization; RBF Networks; Intelligent control; Pattern recognition; Multiresoltuion Analysis; Stochastic search

Hester, Matthew S.Stable Control of Jumping in a Planar Biped Robot
Master of Science, The Ohio State University, 2009, Mechanical Engineering

The ability to perform high-speed dynamic maneuvers is an important aspect of locomotion for bipedal animals such as humans. Running, jumping, and rapidly changing direction are fundamental dynamic maneuvers that contribute to the adaptability and performance required for bipeds to move through unstructured environments. A number of bipedal robots have been produced to investigate dynamic maneuvers. However, the level of performance demonstrated by biological systems has yet to be fully realized in a biped robot. One limiting factor in achieving comparable performance to animals is the lack of available control strategies that can successfully coordinate dynamic maneuvers. This thesis develops a control strategy for producing vertical jumping in a planar biped robot as a preliminary investigation into dynamic maneuvers. The control strategy was developed using a modular approach to allow adaptation to further dynamic maneuvers and robotic systems.

The control strategy was broken into two functional levels to separately solve the problems of planning and performing the jump maneuver. The jump is performed using a low-level controller, consisting of a state machine for determining the current phase of the jump and motor primitives for executing the joint motions required by the current phase. The motor primitives, described by open- and closed-loop control laws, were defined with numeric control parameters for modifying their performance. The high-level controller performs the task of planning the motion required to achieve the desired jump height. Fuzzy control, an intelligent control approach, was selected for the high-level controller. The fuzzy controller uses heuristic information about the biped system to select appropriate control parameters. This heuristic knowledge was implemented in a training algorithm. The training algorithm uses iterative jumps with error-based feedback to determine the control parameters to be implemented by the fuzzy controller.

The control strategy was developed and validated using a numerical simulation of the experimental biped KURMET. The simulation models the dynamics of the biped system and has demonstrated the ability of the control strategy to produce stable successive jumps with an approximate height of 0.575 m. The control strategy was also implemented on the experimental biped for a simplified case, resulting in stable successive jumps with a range of heights from 0.55 to 0.60 m.

Committee:

James Schmiedeler (Advisor); David Orin (Committee Member); Chia-Hsiang Menq (Committee Member)

Subjects:

Electrical Engineering; Engineering; Mechanical Engineering

Keywords:

biped;robot;KURMET;legged;locomotion;jump;jumping;robotics;fuzzy control;intelligent control;control strategy;

Abolaeha, Osama AbohamiaraSmart Growing Rod Device for the Treatment of Early Onset Scoliosis
Doctor of Philosophy (Ph.D.), University of Dayton, 2013, Electrical Engineering
Early Onset Scoliosis (EOS) occurs in children under 10 years of age and many cases have a higher probability of progression during growth. The EOS has been treated with “growing rod” procedure to avoid interference with spinal growth. Patients have to undergo a series of operations to have the rod lengthened for maintaining the correction without affecting the growth of the spine. Adjusting the rods requiring major surgery, costly, and is associated with negative psychosocial outcomes. In an attempt to solve these problems, we have developed a new smart medical device namely, Smart Growing Rod Device (SGRD), that proposes treating EOS with less invasive procedures to minimize the complications associated with the current techniques as well as reducing cost and improving treatment control. This innovative device will have an internal control system, allowing the growing rod to be adjusted based on neural network estimated monthly growth value and a pressure sensor, which determines when the optimum length has been reached. This is accomplished without X rays or other scanning. This study investigates the proposed SGRD for the treatment of EOS via testing our prototype smart growing device with scoliosis model and also with a spine finite element model. The results of those models are very promising and demonstrate system function and effectiveness for treatment of EOS. This will improve quality of life for scoliosis patients, and is more cost-effective than is the traditional growing rod procedure.

Committee:

John Weber (Advisor)

Subjects:

Biomechanics; Biomedical Engineering; Electrical Engineering

Keywords:

intelligent control systems; Early Onset Scoliosis;trajectory generation algorithm; finite element method