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- All Subjects: Electrical Engineering
Description
For a conventional quadcopter system with 4 planar rotors, flight times vary between 10 to 20 minutes depending on the weight of the quadcopter and the size of the battery used. In order to increase the flight time, either the weight of the quadcopter should be reduced or the battery size should be increased. Another way is to increase the efficiency of the propellers. Previous research shows that ducting a propeller can cause an increase of up to 94 % in the thrust produced by the rotor-duct system. This research focused on developing and testing a quadcopter having a centrally ducted rotor which produces 60 % of the total system thrust and 3 other peripheral rotors. This quadcopter will provide longer flight times while having the same maneuvering flexibility in planar movements.
ContributorsLal, Harsh (Author) / Artemiadis, Panagiotis (Thesis advisor) / Lee, Hyunglae (Committee member) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2019
Description
Coordination and control of Intelligent Agents as a team is considered in this thesis.
Intelligent agents learn from experiences, and in times of uncertainty use the knowl-
edge acquired to make decisions and accomplish their individual or team objectives.
Agent objectives are defined using cost functions designed uniquely for the collective
task being performed. Individual agent costs are coupled in such a way that group ob-
jective is attained while minimizing individual costs. Information Asymmetry refers
to situations where interacting agents have no knowledge or partial knowledge of cost
functions of other agents. By virtue of their intelligence, i.e., by learning from past
experiences agents learn cost functions of other agents, predict their responses and
act adaptively to accomplish the team’s goal.
Algorithms that agents use for learning others’ cost functions are called Learn-
ing Algorithms, and algorithms agents use for computing actuation (control) which
drives them towards their goal and minimize their cost functions are called Control
Algorithms. Typically knowledge acquired using learning algorithms is used in con-
trol algorithms for computing control signals. Learning and control algorithms are
designed in such a way that the multi-agent system as a whole remains stable during
learning and later at an equilibrium. An equilibrium is defined as the event/point
where cost functions of all agents are optimized simultaneously. Cost functions are
designed so that the equilibrium coincides with the goal state multi-agent system as
a whole is trying to reach.
In collective load transport, two or more agents (robots) carry a load from point
A to point B in space. Robots could have different control preferences, for example,
different actuation abilities, however, are still required to coordinate and perform
load transport. Control preferences for each robot are characterized using a scalar
parameter θ i unique to the robot being considered and unknown to other robots.
With the aid of state and control input observations, agents learn control preferences
of other agents, optimize individual costs and drive the multi-agent system to a goal
state.
Two learning and Control algorithms are presented. In the first algorithm(LCA-
1), an existing work, each agent optimizes a cost function similar to 1-step receding
horizon optimal control problem for control. LCA-1 uses recursive least squares as
the learning algorithm and guarantees complete learning in two time steps. LCA-1 is
experimentally verified as part of this thesis.
A novel learning and control algorithm (LCA-2) is proposed and verified in sim-
ulations and on hardware. In LCA-2, each agent solves an infinite horizon linear
quadratic regulator (LQR) problem for computing control. LCA-2 uses a learning al-
gorithm similar to line search methods, and guarantees learning convergence to true
values asymptotically.
Simulations and hardware implementation show that the LCA-2 is stable for a
variety of systems. Load transport is demonstrated using both the algorithms. Ex-
periments running algorithm LCA-2 are able to resist disturbances and balance the
assumed load better compared to LCA-1.
Intelligent agents learn from experiences, and in times of uncertainty use the knowl-
edge acquired to make decisions and accomplish their individual or team objectives.
Agent objectives are defined using cost functions designed uniquely for the collective
task being performed. Individual agent costs are coupled in such a way that group ob-
jective is attained while minimizing individual costs. Information Asymmetry refers
to situations where interacting agents have no knowledge or partial knowledge of cost
functions of other agents. By virtue of their intelligence, i.e., by learning from past
experiences agents learn cost functions of other agents, predict their responses and
act adaptively to accomplish the team’s goal.
Algorithms that agents use for learning others’ cost functions are called Learn-
ing Algorithms, and algorithms agents use for computing actuation (control) which
drives them towards their goal and minimize their cost functions are called Control
Algorithms. Typically knowledge acquired using learning algorithms is used in con-
trol algorithms for computing control signals. Learning and control algorithms are
designed in such a way that the multi-agent system as a whole remains stable during
learning and later at an equilibrium. An equilibrium is defined as the event/point
where cost functions of all agents are optimized simultaneously. Cost functions are
designed so that the equilibrium coincides with the goal state multi-agent system as
a whole is trying to reach.
In collective load transport, two or more agents (robots) carry a load from point
A to point B in space. Robots could have different control preferences, for example,
different actuation abilities, however, are still required to coordinate and perform
load transport. Control preferences for each robot are characterized using a scalar
parameter θ i unique to the robot being considered and unknown to other robots.
With the aid of state and control input observations, agents learn control preferences
of other agents, optimize individual costs and drive the multi-agent system to a goal
state.
Two learning and Control algorithms are presented. In the first algorithm(LCA-
1), an existing work, each agent optimizes a cost function similar to 1-step receding
horizon optimal control problem for control. LCA-1 uses recursive least squares as
the learning algorithm and guarantees complete learning in two time steps. LCA-1 is
experimentally verified as part of this thesis.
A novel learning and control algorithm (LCA-2) is proposed and verified in sim-
ulations and on hardware. In LCA-2, each agent solves an infinite horizon linear
quadratic regulator (LQR) problem for computing control. LCA-2 uses a learning al-
gorithm similar to line search methods, and guarantees learning convergence to true
values asymptotically.
Simulations and hardware implementation show that the LCA-2 is stable for a
variety of systems. Load transport is demonstrated using both the algorithms. Ex-
periments running algorithm LCA-2 are able to resist disturbances and balance the
assumed load better compared to LCA-1.
ContributorsKAMBAM, KARTHIK (Author) / Zhang, Wenlong (Thesis advisor) / Nedich, Angelia (Thesis advisor) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2018
Description
Daily collaborative tasks like pushing a table or a couch require haptic communication between the people doing the task. To design collaborative motion planning algorithms for such applications, it is important to understand human behavior. Collaborative tasks involve continuous adaptations and intent recognition between the people involved in the task. This thesis explores the coordination between the human-partners through a virtual setup involving continuous visual feedback. The interaction and coordination are modeled as a two-step process: 1) Collecting data for a collaborative couch-pushing task, where both the people doing the task have complete information about the goal but are unaware of each other's cost functions or intentions and 2) processing the emergent behavior from complete information and fitting a model for this behavior to validate a mathematical model of agent-behavior in multi-agent collaborative tasks. The baseline model is updated using different approaches to resemble the trajectories generated by these models to human trajectories. All these models are compared to each other. The action profiles of both the agents and the position and velocity of the manipulated object during a goal-oriented task is recorded and used as expert-demonstrations to fit models resembling human behaviors. Analysis through hypothesis teasing is also performed to identify the difference in behaviors when there are complete information and information asymmetry among agents regarding the goal position.
ContributorsShintre, Pallavi Shrinivas (Author) / Zhang, Wenlong (Thesis advisor) / Si, Jennie (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2020
Description
This thesis introduces a new robotic leg design with three degrees of freedom that
can be adapted for both bipedal and quadrupedal locomotive systems, and serves as
a blueprint for designers attempting to create low cost robot legs capable of balancing
and walking. Currently, bipedal leg designs are mostly rigid and have not strongly
taken into account the advantages/disadvantages of using an active ankle, as opposed
to a passive ankle, for balancing. This design uses low-cost compliant materials, but
the materials used are thick enough to mimic rigid properties under low stresses, so
this paper will treat the links as rigid materials. A new leg design has been created
that contains three degrees of freedom that can be adapted to contain either a passive
ankle using springs, or an actively controlled ankle using an additional actuator. This
thesis largely aims to focus on the ankle and foot design of the robot and the torque
and speed requirements of the design for motor selection. The dynamics of the system,
including height, foot width, weight, and resistances will be analyzed to determine
how to improve design performance. Model-based control techniques will be used to
control the angle of the leg for balancing. In doing so, it will also be shown that it
is possible to implement model-based control techniques on robots made of laminate
materials.
can be adapted for both bipedal and quadrupedal locomotive systems, and serves as
a blueprint for designers attempting to create low cost robot legs capable of balancing
and walking. Currently, bipedal leg designs are mostly rigid and have not strongly
taken into account the advantages/disadvantages of using an active ankle, as opposed
to a passive ankle, for balancing. This design uses low-cost compliant materials, but
the materials used are thick enough to mimic rigid properties under low stresses, so
this paper will treat the links as rigid materials. A new leg design has been created
that contains three degrees of freedom that can be adapted to contain either a passive
ankle using springs, or an actively controlled ankle using an additional actuator. This
thesis largely aims to focus on the ankle and foot design of the robot and the torque
and speed requirements of the design for motor selection. The dynamics of the system,
including height, foot width, weight, and resistances will be analyzed to determine
how to improve design performance. Model-based control techniques will be used to
control the angle of the leg for balancing. In doing so, it will also be shown that it
is possible to implement model-based control techniques on robots made of laminate
materials.
ContributorsShafa, Taha A (Author) / Aukes, Daniel M (Thesis advisor) / Rogers, Bradley (Committee member) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2020
Description
Touch plays a vital role in maintaining human relationships through social andemotional communications. This research proposes a multi-modal haptic display
capable of generating vibrotactile and thermal haptic signals individually and simultaneously.
The main objective for creating this device is to explore the importance
of touch in social communication, which is absent in traditional communication
modes like a phone call or a video call. By studying how humans interpret
haptically generated messages, this research aims to create a new communication
channel for humans. This novel device will be worn on the user's forearm and has
a broad scope of applications such as navigation, social interactions, notifications,
health care, and education. The research methods include testing patterns in the
vibro-thermal modality while noting its realizability and accuracy. Different patterns
can be controlled and generated through an Android application connected to
the proposed device via Bluetooth. Experimental results indicate that the patterns
SINGLE TAP and HOLD/SQUEEZE were easily identifiable and more relatable to
social interactions. In contrast, other patterns like UP-DOWN, DOWN-UP, LEFTRIGHT,
LEFT-RIGHT, LEFT-DIAGONAL, and RIGHT-DIAGONAL were less
identifiable and less relatable to social interactions. Finally, design modifications
are required if complex social patterns are needed to be displayed on the forearm.
ContributorsGharat, Shubham Shriniwas (Author) / McDaniel, Troy (Thesis advisor) / Redkar, Sangram (Thesis advisor) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2021