This thesis deals with robot path planning. It contains an overview of general approaches for path planning and describes methods of swarm intelligence and their application for robot path planning. This paper also contains proposals of adjustments for ant algorithms and it presents experimental results of algorithm implementation.

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<p>Tactile sensing is an essential human ability for understanding their surroundings. It allows humans to detect and manipulate objects that are concealed or difficult to see in low-light settings. Further, tactile sensing enables people to comprehend object and surface properties that cannot be obtained through visual feedback alone. This is achieved with gentle touches, enabling tactile exploration of fragile, sensitive objects, or living organisms. This capability could be transferred to robots through suitable hardware and algorithms. Nevertheless, current tactile sensors and skills for robotics are not comparable to the tactile sense of humans, thus resulting in inferior characterization of scenes and a risk of altering object states.</p> <p><br></p> <p>To address these limitations, this dissertation proposes a novel framework for robot active tactile exploration and object characterization. The framework combines bioinspired soft sensors and minimally invasive tactile exploration strategies to minimize perturbations to objects. This framework was achieved by: (1) an ultrasensitive whisker sensor that enables object characterization with minimal interaction forces; (2) autonomous tactile exploration skills to localize objects and then characterize their shape and surface properties; and (3) machine learning techniques to analyze contact information gathered by our tactile sensors, enabling the understanding of object attributes by tactile sensing alone. </p> <p><br></p> <p>Experiments were conducted to validate the effectiveness of the framework. In terms of object localization efficiency, informative path planners and contour exploration patterns outperformed baseline methods. Furthermore, the whisker sensor was successfully employed to characterize object surface and liquid properties. Finally, the features found through the characterization process allowed for successful classification by machine learning techniques. These results indicate that the proposed framework can effectively gather multimodal features from environments while maintaining the safety of objects. </p>

Many of the applications that have been proposed for future small unmanned aerial vehicles (UAVs) are at low altitude in areas with many obstacles. A vital component for successful navigation in such environments is a path planner that can find collision free paths for the UAV. Two popular path planning algorithms are the probabilistic roadmap algorithm (PRM) and the rapidly-exploring random tree algorithm (RRT). Adaptations of these algorithms to an unmanned autonomous helicopter are presented in this thesis, together with a number of extensions for handling constraints at different stages of the planning process. The result of this work is twofold: First, the described planners and extensions have been implemented and integrated into the software architecture of a UAV. A number of flight tests with these algorithms have been performed on a physical helicopter and the results from some of them are presented in this thesis. Second, an empirical study has been conducted, comparing the performance of the different algorithms and extensions in this planning domain. It is shown that with the environment known in advance, the PRM algorithm generally performs better than the RRT algorithm due to its precompiled roadmaps, but that the latter is also usable as long as the environment is not too complex. The study also shows that simple geometric constraints can be added in the runtime phase of the PRM algorithm, without a big impact on performance. It is also shown that postponing the motion constraints to the runtime phase can improve the performance of the planner in some cases. / <p>Report code: LiU–Tek–Lic–2006:10.</p>

京都大学 / 新制・課程博士 / 博士(工学) / 甲第24604号 / 工博第5110号 / 新制||工||1978(附属図書館) / 京都大学大学院工学研究科機械理工学専攻 / (主査)教授 松野 文俊, 教授 小森 雅晴, 教授 神田 崇行 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM

This thesis addresses the improvement of cooperative task allocation to vehicles in multiple-vehicle, multiple-target scenarios through the use of more effective preplanned tours. Effective allocation of vehicles to targets requires knowledge of both the team objectives and the contributions that individual vehicles can make toward accomplishing team goals. This is primarily an issue of individual vehicle path planning --- determining the path the vehicles will follow to accomplish individual and team goals. Conventional methods plan optimal point-to-point path segments that often result in lengthy and suboptimal tours because the trajectory neither considers future tasks nor the overall path. However, cooperation between agents is improved when the team selects vehicle assignments based on the ability to complete immediate and subsequent tasks. This research demonstrates that planning more efficient tour paths through multiple targets results in better use of individual vehicle resources, faster completion of team objectives, and improved overall cooperation between agents. This research presents a method of assigning unmanned aerial vehicles to targets to improve cooperation. A tour path planning method was developed to overcome shortcomings of traditional point-to-point path planners, and is extended to the specific tour-planning needs of this problem. The planner utilizes an on-line learning heuristic search to find paths that accomplish team goals in the shortest flight time. The learning search planner uses the entire sensor footprint, more efficiently plans tours through closely packed targets, and learns the best order for completion of the multiple tasks. The improved planner results in assignment completion times that range on average between 1.67 and 2.41 times faster, depending on target spread. Assignments created from preplanned tours make better use of vehicle resources and improve team cooperation. Path planning and assignment selection are accomplished in near real-time through the use of path heuristics and assignment cost estimates to reduce the problem size to tractable levels. Assignments are ordered according to estimated or predicted value. A reduced number of ordered assignments is considered and evaluated to control problem growth. The estimates adequately represent the actual assignment value, effectively reduce problem size, and produce near-optimal paths and assignments for near-real-time applications.