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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 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
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Description
Myoelectric control is lled with potential to signicantly change human-robot interaction.

Humans desire compliant robots to safely interact in dynamic environments

associated with daily activities. As surface electromyography non-invasively measures

limb motion intent and correlates with joint stiness during co-contractions,

it has been identied as a candidate for naturally controlling such robots. However,

state-of-the-art myoelectric

Myoelectric control is lled with potential to signicantly change human-robot interaction.

Humans desire compliant robots to safely interact in dynamic environments

associated with daily activities. As surface electromyography non-invasively measures

limb motion intent and correlates with joint stiness during co-contractions,

it has been identied as a candidate for naturally controlling such robots. However,

state-of-the-art myoelectric interfaces have struggled to achieve both enhanced

functionality and long-term reliability. As demands in myoelectric interfaces trend

toward simultaneous and proportional control of compliant robots, robust processing

of multi-muscle coordinations, or synergies, plays a larger role in the success of the

control scheme. This dissertation presents a framework enhancing the utility of myoelectric

interfaces by exploiting motor skill learning and

exible muscle synergies for

reliable long-term simultaneous and proportional control of multifunctional compliant

robots. The interface is learned as a new motor skill specic to the controller,

providing long-term performance enhancements without requiring any retraining or

recalibration of the system. Moreover, the framework oers control of both motion

and stiness simultaneously for intuitive and compliant human-robot interaction. The

framework is validated through a series of experiments characterizing motor learning

properties and demonstrating control capabilities not seen previously in the literature.

The results validate the approach as a viable option to remove the trade-o

between functionality and reliability that have hindered state-of-the-art myoelectric

interfaces. Thus, this research contributes to the expansion and enhancement of myoelectric

controlled applications beyond commonly perceived anthropomorphic and

\intuitive control" constraints and into more advanced robotic systems designed for

everyday tasks.
ContributorsIson, Mark (Author) / Artemiadis, Panagiotis (Thesis advisor) / Santello, Marco (Committee member) / Greger, Bradley (Committee member) / Berman, Spring (Committee member) / Sugar, Thomas (Committee member) / Fainekos, Georgios (Committee member) / Arizona State University (Publisher)
Created2015
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Description
The interaction between humans and robots has become an important area of research as the diversity of robotic applications has grown. The cooperation of a human and robot to achieve a goal is an important area within the physical human-robot interaction (pHRI) field. The expansion of this field is toward

The interaction between humans and robots has become an important area of research as the diversity of robotic applications has grown. The cooperation of a human and robot to achieve a goal is an important area within the physical human-robot interaction (pHRI) field. The expansion of this field is toward moving robotics into applications in unstructured environments. When humans cooperate with each other, often there are leader and follower roles. These roles may change during the task. This creates a need for the robotic system to be able to exchange roles with the human during a cooperative task. The unstructured nature of the new applications in the field creates a need for robotic systems to be able to interact in six degrees of freedom (DOF). Moreover, in these unstructured environments, the robotic system will have incomplete information. This means that it will sometimes perform an incorrect action and control methods need to be able to correct for this. However, the most compelling applications for robotics are where they have capabilities that the human does not, which also creates the need for robotic systems to be able to correct human action when it detects an error. Activity in the brain precedes human action. Utilizing this activity in the brain can classify the type of interaction desired by the human. For this dissertation, the cooperation between humans and robots is improved in two main areas. First, the ability for electroencephalogram (EEG) to determine the desired cooperation role with a human is demonstrated with a correct classification rate of 65%. Second, a robotic controller is developed to allow the human and robot to cooperate in six DOF with asymmetric role exchange. This system allowed human-robot cooperation to perform a cooperative task at 100% correct rate. High, medium, and low levels of robotic automation are shown to affect performance, with the human making the greatest numbers of errors when the robotic system has a medium level of automation.
ContributorsWhitsell, Bryan Douglas (Author) / Artemiadis, Panagiotis (Thesis advisor) / Santello, Marco (Committee member) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Polygerinos, Panagiotis (Committee member) / Arizona State University (Publisher)
Created2017