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- All Subjects: Mechanical Engineering
Description
The presented work in this report is about Real time Estimation of wind and analyzing current wind correction algorithm in commercial off the shelf Autopilot board. The open source ArduPilot Mega 2.5 (APM 2.5) board manufactured by 3D Robotics is used. Currently there is lot of development being done in the field of Unmanned Aerial Systems (UAVs), various aerial platforms and corresponding; autonomous systems for them. This technology has advanced to such a stage that UAVs can be used for specific designed missions and deployed with reliability. But in some areas like missions requiring high maneuverability with greater efficiency is still under research area. This would help in increasing reliability and augmenting range of UAVs significantly. One of the problems addressed through this thesis work is, current autopilot systems have algorithm that handles wind by attitude correction with appropriate Crab angle. But the real time wind vector (direction) and its calculated velocity is based on geometrical and algebraic transformation between ground speed and air speed vectors. This method of wind estimation and prediction, many a times leads to inaccuracy in attitude correction. The same has been proved in the following report with simulation and actual field testing. In later part, new ways to tackle while flying windy conditions have been proposed.
ContributorsBiradar, Anandrao Shesherao (Author) / Saripalli, Srikanth (Thesis advisor) / Berman, Spring (Thesis advisor) / Thanga, Jekan (Committee member) / Arizona State University (Publisher)
Created2014
Description
The ocean is vital to the health of our planet but remains virtually unexplored. Many researchers seek to understand a wide range of geological and biological phenomena by developing technologies which enable exploration of the deep-sea. The task of developing a technology which can withstand extreme pressure and temperature gradients in the deep ocean is not trivial. Of these technologies, underwater vehicles were developed to study the deep ocean, but remain large and expensive to manufacture. I am proposing the development of cost efficient miniaturized underwater vehicle (mUV) with propulsion systems to carry small measurement devices and enable deep-sea exploration. These mUV's overall size is optimized based on the vehicle parameters such as energy density, desired velocity, swimming time and propulsion performance. However, there are limitations associated with the size of the mUV which leads to certain challenges. For example, 2000 m below the sea level, the pressure is as high as 3000 psi. Therefore, certain underwater vehicle modules, such as the propulsion system, will require pressure housing to ensure the functionality of the thrust generation. In the case of a mUV swimming against the deep-sea current, a thrust magnitude is required to enable the vehicle to overcome the ocean current speed and move forward. Therefore, the size of the mUV is limited by the energy density and the propeller size. An equation is derived to miniaturize underwater vehicle while performing with a certain specifications. An inrunner three-phase permanent magnet brushless DC motor is designed and fabricated with a specific size to fit inside the mUV's core. The motor is composed of stator winding in a pressure housing and an open to water ring-propeller rotor magnet. Several ring-propellers are 3D printed and tested experimentally to determine their performances and efficiencies. A planer motion optimal trajectory for the mUV is determined to minimize the energy usage. Those studies enable the design of size optimized underwater vehicle with propulsion to carry small measurement sensors and enable underwater exploration. Developing mUV's will enable ocean exploration that can lead to significant scientific discoveries and breakthroughs that will solve current world health and environmental problems.
ContributorsMerza, Saeed A (Author) / Meldrum, Deirdre R (Thesis advisor) / Chao, Shih-hui (Committee member) / Shankar, Praveen (Committee member) / Saripalli, Srikanth (Committee member) / Berman, Spring Melody (Committee member) / Arizona State University (Publisher)
Created2014
Description
The traditional understanding of robotics includes mechanisms of rigid structures, which can manipulate surrounding objects, taking advantage of mechanical actuators such as motors and servomechanisms. Although these methods provide the underlying fundamental concepts behind much of modern technological infrastructure, in fields such as manufacturing, automation, and biomedical application, the robotic structures formed by rigid axels on mechanical actuators lack the delicate differential sensors and actuators associated with known biological systems. The rigid structures of traditional robotics also inhibit the use of simple mechanisms in congested and/or fragile environments. By observing a variety of biological systems, it is shown that nature models its structures over millions of years of evolution into a combination of soft structures and rigid skeletal interior supports. Through technological bio-inspired designs, researchers hope to mimic some of the complex behaviors of biological mechanisms using pneumatic actuators coupled with highly compliant materials that exhibit relatively large reversible elastic strain. This paper begins the brief history of soft robotics, the various classifications of pneumatic fluid systems, the associated difficulties that arise with the unpredictable nature of fluid reactions, the methods of pneumatic actuators in use today, the current industrial applications of soft robotics, and focuses in large on the construction of a universally adaptable soft robotic gripper and material application tool. The central objective of this experiment is to compatibly pair traditional rigid robotics with the emerging technologies of sort robotic actuators. This will be done by combining a traditional rigid robotic arm with a soft robotic manipulator bladder for the purposes of object manipulation and excavation of extreme environments.
ContributorsShuster, Eden S. (Author) / Thanga, Jekan (Thesis director) / Asphaug, Erik (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Technological progress in robot sensing, design, and fabrication, and the availability of open source software frameworks such as the Robot Operating System (ROS), are advancing the applications of swarm robotics from toy problems to real-world tasks such as surveillance, precision agriculture, search-and-rescue, and infrastructure inspection. These applications will require the development of robot controllers and system architectures that scale well with the number of robots and that are robust to robot errors and failures. To achieve this, one approach is to design decentralized robot control policies that require only local sensing and local, ad-hoc communication. In particular, stochastic control policies can be designed that are agnostic to individual robot identities and do not require a priori information about the environment or sophisticated computation, sensing, navigation, or communication capabilities. This dissertation presents novel swarm control strategies with these properties for detecting and mapping static targets, which represent features of interest, in an
unknown, bounded, obstacle-free environment. The robots move on a finite spatial grid according to
the time-homogeneous transition probabilities of a Discrete-Time Discrete-State (DTDS) Markov chain model, and they exchange information with other robots within their communication range using a consensus (agreement) protocol. This dissertation extend theoretical guarantees on multi-robot consensus over fixed and time-varying communication networks with known connectivity properties to consensus over the networks that have Markovian switching dynamics and no presumed connectivity. This dissertation develops such swarm consensus strategies for detecting a single feature in the environment, tracking multiple features, and reconstructing a discrete distribution of features modeled as an occupancy grid map.
The proposed consensus approaches are validated in numerical simulations and in 3D physics-based simulations of quadrotors in Gazebo. The scalability of the proposed approaches is examined through extensive numerical simulation studies over different swarm populations and environment sizes.
ContributorsShirsat, Aniket (Author) / Berman, Spring (Thesis advisor) / Lee, Hyunglae (Committee member) / Marvi, Hamid (Committee member) / Saripalli, Srikanth (Committee member) / Gharavi, Lance (Committee member) / Arizona State University (Publisher)
Created2022