Action planning has been used in the field of robotics for solving long-running tasks. In the robot architectures field, it is also known as the deliberative layer. However, there is still a gap between the symbolic representation on the one hand and the low-level control and sensor representation on the other. In addition, the definition of a planning problem for a complex, real-world robot is not trivial. The planning process could become intractable as its search spaces become large. As the defined planning problem determines the complexity and the computationability for solving the problem, it should contain only relevant states. In this work, a novel approach which amalgamates Description Logic (DL) reasoning with Hierarchical Task Network (HTN) planning is introduced. The planning domain description as well as fundamental HTN planning concepts are represented in DL and can therefore be subject to DL reasoning; from these representations, concise planning problems are generated for HTN planning. The method is presented through an example in the robot navigation domain. In addition, a case study of the RoboCup@Home domain is given. As proof of concept, a well-known planning problem that often serves as a benchmark, namely that of the blocks-world, is modeled and solved using this approach. An analysis of the performance of the approach has been conducted and the results show that this approach yields significantly smaller planning problem descriptions than those generated by current representations in HTN planning.

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<p>Multirotor unmanned aerial vehicles (UAVs) added with the ability to physically interact with the environment has opened endless possibilities for aerial mobile manipulation tasks. With the unlimited reachable workspace and physical interaction capabilities, such robots can enhance human ability to perform dangerous and hard-to-reach tasks. However, realizing aerial mobile manipulation in real-world scenarios is challenging with respect to the diversity in aerial platforms, control fidelity and susceptibility to variations in the environment. Therefore, the first part of the dissertation provides tools to  classify and evaluate different multirotor designs. A measure of responsiveness of a multirotor platform in exerting generalized forces and rejecting disturbances is discussed through the control bandwidth analysis. Superiority in control bandwidth for fully-actuated multirotors is established in a comparison with equivalent under-actuated multirotors. To further classify and distinguish multirotor platforms, a new mobility measure is proposed and compared by surveying all aerial platforms employed for aerial mobile manipulation. In compliance to the control bandwidth analysis, the mobility measure for fully-actuated multirotors is relatively higher making them better suited for manipulation tasks. </p> <p><br></p> <p><br></p> <p>Aerial physical interaction, as a part of aerial mobile manipulation, with partially unknown environments is challenging due to the uncertainties imposed while dexterously exerting force signatures. A hybrid physical interaction (HyPhI) controller is proposed to enable constrained force contact with a steady transition from unconstrained motion, by squelching excess energy during initial impact. However, uncertainties posed by the partially unknown environment requires to understand the surrounding environment and their current physical states, that can enhance interaction performance. The limited resources and flight time of the multirotors requires to simultaneously understand the environment and perform aerial physical interactions. Inspection-on-the-fly is an uncanny ability of humans to intuitively infer states during manipulation while reducing the necessity to conduct inspection and manipulation separately. In this dissertation, the inspection-on-the-fly method based HyPhI controller is proposed to engage in a steady contact with partially unknown environments, while simultaneously estimating the physical states of the surfaces. The proposed method is evaluated in a mockup of real-world facility, to understand the surface properties while engaging in steady interactions. Further, such inspection of surfaces and estimation of various states enables a deeper understanding of the environment while enhancing the ability to physically interact. </p>

The thesis research aims to further the study of human-robot interaction (HRI) issues, especially regarding the development of an assistive robot designed to help individuals possessing motor impairments. In particular, individuals with amyotrophic lateral sclerosis (ALS), represent a potential user population that possess an array of motor impairment due to the progressive nature of the disease. Through review of the literature, an initial target for robotic assistance was determined to be object retrieval and delivery tasks to aid with dropped or otherwise unreachable objects, which represent a common and significant difficulty for individuals with limited motor capabilities. This thesis research has been conducted as part of a larger, collaborative project between the Georgia Institute of Technology and Emory University. To this end, we developed and evaluated a semi-autonomous mobile healthcare service robot named EL-E. I conducted four human studies involving patients with ALS with the following objectives: 1) to investigate and better understand the practical, everyday needs and limitations of people with severe motor impairments; 2) to translate these needs into pragmatic tasks or goals to be achieved through an assistive robot and reflect these needs and limitations into the robot's design; 3) to develop practical, usable, and effective interaction mechanisms by which the impaired users can control the robot; and 4) and to evaluate the performance of the robot and improve its usability. I anticipate that the findings from this research will contribute to the ongoing research in the development and evaluation of effective and affordable assistive manipulation robots, which can help to mitigate the difficulties, frustration, and lost independence experienced by individuals with significant motor impairments and improve their quality of life.