Matching Items (24)
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- All Subjects: robotics
- Genre: Academic theses
- Creators: Yong, Sze Zheng
- Creators: Das, Jnaneshwar
Description
The construction industry is very mundane and tiring for workers without the assistance of machines. This challenge has changed the trend of construction industry tremendously by motivating the development of robots that can replace human workers. This thesis presents a computed torque controller that is designed to produce movements by a small-scale, 5 degree-of-freedom (DOF) robotic arm that are useful for construction operations, specifically bricklaying. A software framework for the robotic arm with motion and path planning features and different control capabilities has also been developed using the Robot Operating System (ROS).
First, a literature review of bricklaying construction activity and existing robots’ performance is discussed. After describing an overview of the required robot structure, a mathematical model is presented for the 5-DOF robotic arm. A model-based computed torque controller is designed for the nonlinear dynamic robotic arm, taking into consideration the dynamic and kinematic properties of the arm. For sustainable growth of this technology so that it is affordable to the masses, it is important that the energy consumption by the robot is optimized. In this thesis, the trajectory of the robotic arm is optimized using sequential quadratic programming. The results of the energy optimization procedure are also analyzed for different possible trajectories.
A construction testbed setup is simulated in the ROS platform to validate the designed controllers and optimized robot trajectories on different experimental scenarios. A commercially available 5-DOF robotic arm is modeled in the ROS simulators Gazebo and Rviz. The path and motion planning is performed using the Moveit-ROS interface and also implemented on a physical small-scale robotic arm. A Matlab-ROS framework for execution of different controllers on the physical robot is described. Finally, the results of the controller simulation and experiments are discussed in detail.
First, a literature review of bricklaying construction activity and existing robots’ performance is discussed. After describing an overview of the required robot structure, a mathematical model is presented for the 5-DOF robotic arm. A model-based computed torque controller is designed for the nonlinear dynamic robotic arm, taking into consideration the dynamic and kinematic properties of the arm. For sustainable growth of this technology so that it is affordable to the masses, it is important that the energy consumption by the robot is optimized. In this thesis, the trajectory of the robotic arm is optimized using sequential quadratic programming. The results of the energy optimization procedure are also analyzed for different possible trajectories.
A construction testbed setup is simulated in the ROS platform to validate the designed controllers and optimized robot trajectories on different experimental scenarios. A commercially available 5-DOF robotic arm is modeled in the ROS simulators Gazebo and Rviz. The path and motion planning is performed using the Moveit-ROS interface and also implemented on a physical small-scale robotic arm. A Matlab-ROS framework for execution of different controllers on the physical robot is described. Finally, the results of the controller simulation and experiments are discussed in detail.
ContributorsGandhi, Sushrut (Author) / Berman, Spring (Thesis advisor) / Marvi, Hamidreza (Committee member) / Yong, Sze Zheng (Committee member) / Arizona State University (Publisher)
Created2019
Description
Soft Poly-Limb (SPL) is a pneumatically driven, wearable, soft continuum robotic arm designed to aid humans with medical conditions, such as cerebral palsy, paraplegia, cervical spondylotic myelopathy, perform activities of daily living. To support user's tasks, the SPL acts as an additional limb extending from the human body which can be controlled to perform safe and compliant mobile manipulation in three-dimensional space. The SPL is inspired by invertebrate limbs, such as the elephant trunk and the arms of the octopus. In this work, various geometrical and physical parameters of the SPL are identified, and behavior of the actuators that comprise it are studied by varying their parameters through novel quasi-static computational models. As a result, this study provides a set of engineering design rules to create soft actuators for continuum soft robotic arms by understanding how varying parameters affect the actuator's motion as a function of the input pressure. A prototype of the SPL is fabricated to analyze the accuracy of these computational models by performing linear expansion, bending and arbitrary pose tests. Furthermore, combinations of the parameters based on the application of the SPL are determined to affect the weight, payload capacity, and stiffness of the arm. Experimental results demonstrate the accuracy of the proposed computational models and help in understanding the behavior of soft compliant actuators. Finally, based on the set functional requirements for the assistance of impaired users, results show the effectiveness of the SPL in performing tasks for activities of daily living.
ContributorsNuthi, Sai Gautham (Author) / Polygerinos, Panagiotis (Thesis advisor) / Lee, Hyunglae (Committee member) / Yong, Sze Zheng (Committee member) / Arizona State University (Publisher)
Created2018
Description
This work considers the design of separating input signals in order to discriminate among a finite number of uncertain nonlinear models. Each nonlinear model corresponds to a system operating mode, unobserved intents of other drivers or robots, or to fault types or attack strategies, etc., and the separating inputs are designed such that the output trajectories of all the nonlinear models are guaranteed to be distinguishable from each other under any realization of uncertainties in the initial condition, model discrepancies or noise. I propose a two-step approach. First, using an optimization-based approach, we over-approximate nonlinear dynamics by uncertain affine models, as abstractions that preserve all its system behaviors such that any discrimination guarantees for the affine abstraction also hold for the original nonlinear system. Then, I propose a novel solution in the form of a mixed-integer linear program (MILP) to the active model discrimination problem for uncertain affine models, which includes the affine abstraction and thus, the nonlinear models. Finally, I demonstrate the effectiveness of our approach for identifying the intention of other vehicles in a highway lane changing scenario. For the abstraction, I explore two approaches. In the first approach, I construct the bounding planes using a Mixed-Integer Nonlinear Problem (MINLP) formulation of the given system with appropriately designed constraints. For the second approach, I solve a linear programming (LP) problem that over-approximates the nonlinear function at only the grid points of a mesh with a given resolution and then accounting for the entire domain via an appropriate correction term. To achieve a desired approximation accuracy, we also iteratively subdivide the domain into subregions. This method applies to nonlinear functions with different degrees of smoothness, including Lipschitz continuous functions, and improves on existing approaches by enabling the use of tighter bounds. Finally, we compare the effectiveness of this approach with the existing optimization-based methods in simulation and illustrate its applicability for estimator design.
ContributorsSingh, Kanishka Raj (Author) / Yong, Sze Zheng (Thesis advisor) / Artemiadis, Panagiotis (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2018
Description
The ability for aerial manipulators to stay aloft while interacting with dynamic environments is critical for successfully in situ data acquisition methods in arboreal environments. One widely used platform utilizes a six degree of freedom manipulator attached to quadcoper or octocopter, to sample a tree leaf by maintaining the system in a hover while the arm pulls the leaf for a sample. Other system are comprised of simpler quadcopter with a fixed mechanical device to physically cut the leaf while the system is manually piloted. Neither of these common methods account or compensate for the variation of inherent dynamics occurring in the arboreal-aerial manipulator interaction effects. This research proposes force and velocity feedback methods to control an aerial manipulation platform while allowing waypoint navigation within the work space to take place. Using these methods requires minimal knowledge of the system and the dynamic parameters. This thesis outlines the Robot Operating System (ROS) based Open Autonomous Air Vehicle (OpenUAV) simulations performed on the purposed three degree of freedom redundant aerial manipulation platform.
ContributorsCohen, Daniel (Author) / Das, Jnaneshwar (Thesis advisor) / Marvi, Hamidreza (Committee member) / Saldaña, David (Committee member) / Arizona State University (Publisher)
Created2022
Description
Autonomous Robots have a tremendous potential to assist humans in environmental monitoring tasks. In order to generate meaningful data for humans to analyze, the robots need to collect accurate data and develop reliable representation of the environment. This is achieved by employing scalable and robust navigation and mapping algorithms that facilitate acquiring and understanding data collected from the array of on-board sensors. To this end, this thesis presents navigation and mapping algorithms for autonomous robots that can enable robot navigation in complexenvironments and develop real time semantic map of the environment respectively. The first part of the thesis presents a novel navigation algorithm for an autonomous underwater vehicle that can maintain a fixed distance from the coral terrain while following a human diver. Following a human diver ensures that the robot would visit all important sites in the coral reef while maintaining a constant distance from the terrain reduces heterscedasticity in the measurements. This algorithm was tested on three different synthetic terrains including a real model of a coral reef in Hawaii. The second part of the thesis presents a dense semantic surfel mapping technique based on top of a popular surfel mapping algorithm that can generate meaningful maps in real time. A semantic mask from a depth aligned RGB-D camera was used to assign labels
to the surfels which were then probabilistically updated with multiple measurements. The mapping algorithm was tested with simulated data from an RGB-D camera and the results were analyzed.
ContributorsAntervedi, Lakshmi Gana Prasad (Author) / Das, Jnaneshwar (Thesis advisor) / Martin, Roberta E (Committee member) / Marvi, Hamid (Committee member) / Arizona State University (Publisher)
Created2021
Description
Despite the rapid adoption of robotics and machine learning in industry, their application to scientific studies remains under-explored. Combining industry-driven advances with scientific exploration provides new perspectives and a greater understanding of the planet and its environmental processes. Focusing on rock detection, mapping, and dynamics analysis, I present technical approaches and scientific results of developing robotics and machine learning technologies for geomorphology and seismic hazard analysis. I demonstrate an interdisciplinary research direction to push the frontiers of both robotics and geosciences, with potential translational contributions to commercial applications for hazard monitoring and prospecting. To understand the effects of rocky fault scarp development on rock trait distributions, I present a data-processing pipeline that utilizes unpiloted aerial vehicles (UAVs) and deep learning to segment densely distributed rocks in several orders of magnitude. Quantification and correlation analysis of rock trait distributions demonstrate a statistical approach for geomorphology studies. Fragile geological features such as precariously balanced rocks (PBRs) provide upper-bound ground motion constraints for hazard analysis. I develop an offboard method and onboard method as complementary to each other for PBR searching and mapping. Using deep learning, the offboard method segments PBRs in point clouds reconstructed from UAV surveys. The onboard method equips a UAV with edge-computing devices and stereo cameras, enabling onboard machine learning for real-time PBR search, detection, and mapping during surveillance. The offboard method provides an efficient solution to find PBR candidates in existing point clouds, which is useful for field reconnaissance. The onboard method emphasizes mapping individual PBRs for their complete visible surface features, such as basal contacts with pedestals–critical geometry to analyze fragility. After PBRs are mapped, I investigate PBR dynamics by building a virtual shake robot (VSR) that simulates ground motions to test PBR overturning. The VSR demonstrates that ground motion directions and niches are important factors determining PBR fragility, which were rarely considered in previous studies. The VSR also enables PBR large-displacement studies by tracking a toppled-PBR trajectory, presenting novel methods of rockfall hazard zoning. I build a real mini shake robot providing a reverse method to validate simulation experiments in the VSR.
ContributorsChen, Zhiang (Author) / Arrowsmith, Ramon (Thesis advisor) / Das, Jnaneshwar (Thesis advisor) / Bell, James (Committee member) / Berman, Spring (Committee member) / Christensen, Philip (Committee member) / Whipple, Kelin (Committee member) / Arizona State University (Publisher)
Created2022
Description
This work has improved the quality of the solution to the sparse rewards problemby combining reinforcement learning (RL) with knowledge-rich planning. Classical
methods for coping with sparse rewards during reinforcement learning modify the
reward landscape so as to better guide the learner. In contrast, this work combines
RL with a planner in order to utilize other information about the environment. As
the scope for representing environmental information is limited in RL, this work has
conflated a model-free learning algorithm – temporal difference (TD) learning – with
a Hierarchical Task Network (HTN) planner to accommodate rich environmental
information in the algorithm. In the perpetual sparse rewards problem, rewards
reemerge after being collected within a fixed interval of time, culminating in a lack of a
well-defined goal state as an exit condition to the problem. Incorporating planning in
the learning algorithm not only improves the quality of the solution, but the algorithm
also avoids the ambiguity of incorporating a goal of maximizing profit while using
only a planning algorithm to solve this problem. Upon occasionally using the HTN
planner, this algorithm provides the necessary tweak toward the optimal solution. In
this work, I have demonstrated an on-policy algorithm that has improved the quality
of the solution over vanilla reinforcement learning. The objective of this work has
been to observe the capacity of the synthesized algorithm in finding optimal policies to
maximize rewards, awareness of the environment, and the awareness of the presence
of other agents in the vicinity.
ContributorsNandan, Swastik (Author) / Pavlic, Theodore (Thesis advisor) / Das, Jnaneshwar (Thesis advisor) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2022
Description
Navigation and mapping in GPS-denied environments, such as coal mines ordilapidated buildings filled with smog or particulate matter, pose a significant challenge
due to the limitations of conventional LiDAR or vision systems. Therefore there
exists a need for a navigation algorithm and mapping strategy which do not use vision
systems but are still able to explore and map the environment. The map can further
be used by first responders and cave explorers to access the environments.
This thesis presents the design of a collision-resilient Unmanned Aerial Vehicle
(UAV), XPLORER that utilizes a novel navigation algorithm for exploration and
simultaneous mapping of the environment. The real-time navigation algorithm uses
the onboard Inertial Measurement Units (IMUs) and arm bending angles for contact
estimation and employs an Explore and Exploit strategy. Additionally, the quadrotor
design is discussed, highlighting its improved stability over the previous design.
The generated map of the environment can be utilized by autonomous vehicles to
navigate the environment. The navigation algorithm is validated in multiple real-time
experiments in different scenarios consisting of concave and convex corners and circular
objects. Furthermore, the developed mapping framework can serve as an auxiliary
input for map generation along with conventional LiDAR or vision-based mapping
algorithms.
Both the navigation and mapping algorithms are designed to be modular, making
them compatible with conventional UAVs also. This research contributes to the
development of navigation and mapping techniques for GPS-denied environments,
enabling safer and more efficient exploration of challenging territories.
ContributorsPandian Saravanakumaran, Aravind Adhith (Author) / Zhang, Wenlong (Thesis advisor) / Das, Jnaneshwar (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2023
Description
This thesis considers the problem of multi-robot task allocation with inter-agent distance constraints, e.g., due to the presence of physical tethers or communication requirements, that must be satisfied at all times. Specifically, three optimization-based formulations are explored: (i) a “Naive Method” that leverages the classical multiple traveling salesman (mTSP) formulation to find solutions that are then filtered out when the inter-agent distance constraints are violated, (ii) a “Timed Method” thatconstructs a new formulation that explicitly accounts for robot timings, including the inter-agent distance constraints, and (iii) an “Improved Naive Method” that reformulates the Naive Method with a novel graph-traversal algorithm to produce tours that,
unlike the Naive Method, allow backtracking and also introduces a more systematic approach to filter out solutions that violate inter-agent distance constraints. The effectiveness of the approaches to return task allocations that satisfy the constraints
are demonstrated and compared in simulation experiments.
ContributorsGoodwin, Walter Alexander (Author) / Yong, Sze Zheng (Thesis advisor) / Grewal, Anoop (Thesis advisor) / Xu, Zhe (Committee member) / Arizona State University (Publisher)
Created2023
Description
Acrobatic maneuvers of quadrotors present unique challenges concerning trajectorygeneration, control, and execution. Specifically, the flip maneuver requires dynamically
feasible trajectories and precise control. Various factors, including rotor dynamics,
thrust allocation, and control strategies, influence the successful execution of flips.
This research introduces an approach for tracking optimal trajectories to execute flip
maneuvers while ensuring system stability autonomously. Model Predictive Control
(MPC) designs the controller, enabling the quadrotor to plan and execute optimal
trajectories in real-time, accounting for dynamic constraints and environmental factors.
The utilization of predictive models enables the quadrotor to anticipate and adapt to
changes during aggressive maneuvers.
Simulation-based evaluations were conducted in the ROS and Gazebo environments.
These evaluations provide valuable insights into the quadrotor’s behavior, response
time, and tracking accuracy. Additionally, real-time flight experiments utilizing state-
of-the-art flight controllers, such as the PixHawk 4, and companion computers, like
the Hardkernel Odroid, validate the effectiveness of the proposed control algorithms
in practical scenarios. The conducted experiments also demonstrate the successful
execution of the proposed approach.
This research’s outcomes contribute to quadrotor technology’s advancement, particularly in acrobatic maneuverability. This opens up possibilities for executing
maneuvers with precise timing, such as slingshot probe releases during flips. Moreover,
this research demonstrates the efficacy of MPC controllers in achieving autonomous
probe throws within no-fly zone environments while maintaining an accurate desired
range. Field application of this research includes probe deployment into volcanic
plumes or challenging-to-access rocky fault scarps, and imaging of sites of interest. along flight paths through rolling or pitching maneuvers of the quadrotor, to use sensorsuch as cameras or spectrometers on the quadrotor belly.
Contributorsjain, saransh (Author) / Das, Jnaneshwar (Thesis advisor) / Zhang, Wenlong (Committee member) / Marvi, Hamid (Committee member) / Arizona State University (Publisher)
Created2023