Thesis(M.S.)--Auburn University, 2005. / Abstract. Vita. Includes program. Includes bibliographic references.

Thesis (Master)--İzmir Institute of Technology, İzmir, 2004. / Includes bibliographical references (leaves . 71-75).

Mechanical compliance will be an essential component for agile robots as they begin to leave the laboratory settings and join our world. The most crucial finding of this dissertation is showing how lessons learned from soft robotics can be adapted into traditional robotics to introduce compliance. Therefore, it presents practical knowledge on how to build soft bodied sensor and actuation modules: first example being soft-bodied curvature sensors. These sensors contain both standard electronic components soldered on flexible PCBs and hyperelastic materials that cover the electronics. They are built by curing multi-material composites inside hyper elastic materials. Then it shows, via precise sensing by using magnets and Hall-effect sensors, how closed-loop control of soft actuation modules can be achieved via proprioceptive feedback. Once curvature sensing idea is verified, the dissertation describes how the same sensing methodology, along with the same multi-material manufacturing technique can be utilized to construct soft bodied tri-axial force sensors. It shows experimentally that these sensors can be used by traditional robotic grippers to increase grasping quality. At this point, I observe that compliance is an important property that robots may utilize for different types of motions. One example being Raibert's 2D hopper mechanism. It uses its leg-spring to store energy while on the ground and release this energy before jumping. I observe that via soft material design, it would be possible to embed compliance directly into the linkage design itself. So I go over the design details of an extremely lightweight compliant five-bar mechanism design that can store energy when compressed via soft ligaments embedded in its joints. I experimentally show that the compliant leg design offers increased efficiency compared to a rigid counterpart. I also utilize the previously mentioned soft bodied force sensors for rapid contact detection (~5-10 Hz) in the hopper test platform. In the end, this thesis connects soft robotics with the traditional body of robotic knowledge in two aspects: a) I show that manufacturing techniques we use for soft bodied sensor/actuator designs can be utilized for creating soft ligaments that add strength and compliance to robot joints; and b) I demonstrate that soft bodied force sensing techniques can be used reliably for robotic contact detection.

Thesis (M.S.)--West Virginia University, 2001. / Title from document title page. Document formatted into pages; contains ix, 87 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 35-36).

Thesis (M.S. in Astronautical Engineering)--Naval Postgraduate School, September 2006. / "September 2006." Thesis advisor: Romano, Marcello. Performed by Naval Postgraduate School, Monterey, CA. Includes bibliographical references (p. 73-74). Also available online from the Naval Postgraduate School (NPS), Dudley Knox Library site and the DTIC Online site.

Thesis--Auburn University, 2009. / Abstract. Vita. Includes bibliographical references (p. 45-46).

Thesis (Ph. D.)--Brigham Young University. Dept. of Computer Science, 2006. / Includes bibliographical references (p. 149-164).

Scalable autonomy requires a robot to be able to recognize and contend with the uncertainty in its knowledge of the world stemming from its noisy sensors and actu- ators. The regions it chooses to explore, and the paths it takes to get there, must take this uncertainty into account. In this thesis we outline probabilistic approaches to represent that world; to construct plans over it; and to determine which part of it to explore next. We present a new technique to create probabilistic cost maps from overhead im- agery, taking into account the uncertainty in terrain classification and allowing for spatial variation in terrain cost. A probabilistic cost function combines the output of a multi-class classifier and a spatial probabilistic regressor to produce a probability density function over terrain for each grid cell in the map. The resultant cost map facilitates the discovery of not only the shortest path between points on the map, but also a distribution of likely paths between the points. These cost maps are used in a path planning technique which allows the user to trade-off the risk of returning a suboptimal path for substantial increases in search speed. We precompute a probability distribution which precisely approximates the true distance between any grid cell in the map and goal cell. This distribution under- pins a number of A* search heuristics we present, which can characterize and bound the risk we are prepared to take in gaining search efficiency while sacrificing optimal path length. Empirically, we report efficiency increases in excess of 70% over standard heuristic search methods. Finally, we present a global approach to the problem of robotic exploration, uti- lizing a hybrid of a topological data structure and an underlying metric mapping process. A ‘Gap Navigation Tree’ is used to motivate global target selection and occluded regions of the environment (‘gaps’) are tracked probabilistically using the metric map. In pursuing these gaps we are provided with goals to feed to the path planning process en route to a complete exploration of the environment. The combination of these three techniques represents a framework to facilitate robust exploration in a-priori unknown environments.

This thesis addresses the problem of how to control an autonomous mobile robot navigation in indoor environments, in the face of sensor noise, imprecise information, uncertainty and limited response time. The thesis argues that the effective control of autonomous mobile robots can be achieved by organising low level and higher level control activities into a layered architecture. The low level reactive control allows the robot to respond to contingencies quickly. The higher level control allows the robot to make longer term decisions and arranges appropriate sequences for a task execution. The thesis describes the design and implementation of a two layer control architecture, a task template based sequencing layer and a fuzzy behaviour based low level control layer. The sequencing layer works at the pace of the higher level of abstraction, interprets a task plan, mediates and monitors the controlling activities. While the low level performs fast computation in response to dynamic changes in the real world and carries out robust control under uncertainty. The organisation and fusion of fuzzy behaviours are described extensively for the construction of a low level control system. A learning methodology is also developed to systematically learn fuzzy behaviours and the behaviour selection network and therefore solve the difficulties in configuring the low level control layer. A two layer control system has been implemented and used to control a simulated mobile robot performing two tasks in simulated indoor environments. The effectiveness of the layered control and learning methodology is demonstrated through the traces of controlling activities at the two different levels. The results also show a general design methodology that the high level should be used to guide the robot's actions while the low level takes care of detailed control in the face of sensor noise and environment uncertainty in real time.

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