Thesis (M.Sc. (Hons)) -- University of Western Sydney, Nepean, 2000. / "Thesis submitted for the degree of Master of Engineering (Hons), School of Mechatronic, Computer & Electrical Engineering, University of Western Sydney, Nepean" Bibliography : leaves 129-141.

Thesis (M.C.S.) - Carleton University, 2005. / Includes bibliographical references (p. 153-161). Also available in electronic format on the Internet.

Thesis (Ph.D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2008. / Committee Chair: Abler, Randal T.; Committee Member: Copeland, John A.; Committee Member: Haas, Kevin; Committee Member: Moore II, Elliot; Committee Member: Owen III, Henry L.

Thesis (Ph.D)--Computing, Georgia Institute of Technology, 2009. / Committee Chair: Dellaert, Frank; Committee Member: Bobick, Aaron; Committee Member: Christensen, Henrik; Committee Member: Leonard, John; Committee Member: Rehg, James. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2006. / Ashraf Saad, Committee Member ; Spyros Reveliotis, Committee Member ; Anthony Yezzi, Committee Member ; Erik Verriest, Committee Member ; Yorai Wardi, Committee Co-Chair ; Magnus Egerstedt, Committee Chair.

Thesis (Ph. D.)--Oregon State University, 2010. / Printout. Includes bibliographical references (leaves 134-142). Also available on the World Wide Web.

Thesis (MSc (Mathematical Sciences. Computer SCience))--University of Stellenbosch, 2009. / In this thesis we consider the dynamic path planning problem for robotics. The dynamic path planning problem, in short, is the task of determining an optimal path, in terms of minimising a given cost function, from one location to another within a known environment of moving obstacles. Our goal is to investigate a number of well-known path planning algorithms, to determine for which circumstances a particular algorithm is best suited, and to propose changes to existing algorithms to make them perform better in dynamic environments. At this stage no thorough comparison of theoretical and actual running times of path planning algorithms exist. Our main goal is to address this shortcoming by comparing some of the wellknown path planning algorithms and our own improvements to these path planning algorithms in a simulation environment. We show that the visibility graph representation of the environment combined with the A* algorithm provides very good results for both path length and computational cost, for a relatively small number of obstacles. As for a grid representation of the environment, we show that the A* algorithm produces good paths in terms of length and the amount of rotation and it requires less computation than dynamic algorithms such as D* and D* Lite.

Modern applications of mobile robots require them to have the ability to safely and effectively navigate in human environments. New challenges arise when these robots must plan their motion in a human-aware fashion. Current methods addressing this problem have focused mainly on the activity forecasting aspect, aiming at improving predictions without considering the active nature of the interaction, i.e. the robot’s effect on the environment and consequent issues such as reciprocity. Furthermore, many methods rely on computationally expensive offline training of predictive models that may not be well suited to rapidly evolving dynamic environments. This thesis presents a novel approach for enabling autonomous robots to navigate socially in environments with humans. Following formulations of the inverse planning problem, agents reason about the intentions of other agents and make predictions about their future interactive motion. A technique is proposed to implement counterfactual reasoning over a parametrised set of light-weight reciprocal motion models, thus making it more tractable to maintain beliefs over the future trajectories of other agents towards plausible goals. The speed of inference and the effectiveness of the algorithms is demonstrated via physical robot experiments, where computationally constrained robots navigate amongst humans in a distributed multi-sensor setup, able to infer other agents’ intentions as fast as 100ms after the first observation. While intention inference is a key aspect of successful human-robot interaction, executing any task requires planning that takes into account the predicted goals and trajectories of other agents, e.g., pedestrians. It is well known that robots demonstrate unwanted behaviours, such as freezing or becoming sluggishly responsive, when placed in dynamic and cluttered environments, due to the way in which safety margins according to simple heuristics end up covering the entire feasible space of motion. The presented approach makes more refined predictions about future movement, which enables robots to find collision-free paths quickly and efficiently. This thesis describes a novel technique for generating "interactive costmaps", a representation of the planner’s costs and rewards across time and space, providing an autonomous robot with the information required to navigate socially given the estimate of other agents’ intentions. This multi-layered costmap deters the robot from obstructing while encouraging social navigation respectful of other agents’ activity. Results show that this approach minimises collisions and near-collisions, minimises travel times for agents, and importantly offers the same computational cost as the most common costmap alternatives for navigation. A key part of the practical deployment of such technologies is their ease of implementation and configuration. Since every use case and environment is different and distinct, the presented methods use online adaptation to learn parameters of the navigating agents during runtime. Furthermore, this thesis includes a novel technique for allocating tasks in distributed robotics systems, where a tool is provided to maximise the performance on any distributed setup by automatic parameter tuning. All of these methods are implemented in ROS and distributed as open-source. The ultimate aim is to provide an accessible and efficient framework that may be seamlessly deployed on modern robots, enabling widespread use of intention prediction for interactive navigation in distributed robotic systems.

Submitted by Diogo Barreiros (diogo.barreiros@ufba.br) on 2017-03-10T14:39:59Z No. of bitstreams: 1 tese_alexandre_versao_final_bd.pdf: 3780030 bytes, checksum: 765f095f9626a12f3b43a6bf9fdb97f3 (MD5) / Approved for entry into archive by Vanessa Reis (vanessa.jamile@ufba.br) on 2017-03-10T14:52:25Z (GMT) No. of bitstreams: 1 tese_alexandre_versao_final_bd.pdf: 3780030 bytes, checksum: 765f095f9626a12f3b43a6bf9fdb97f3 (MD5) / Made available in DSpace on 2017-03-10T14:52:25Z (GMT). No. of bitstreams: 1 tese_alexandre_versao_final_bd.pdf: 3780030 bytes, checksum: 765f095f9626a12f3b43a6bf9fdb97f3 (MD5) / The automatic person re-identification (re-id) problem resides in matching an unknown person image to a database of previously labeled images of people. Among several issues to cope with this research field, person re-id has to deal with person appearance and environment variations. As such, discriminative features to represent a person identity must be robust regardless those variations. Comparison among two image features is commonly accomplished by distance metrics. Although features and distance metrics can be handcrafted or trainable, the latter type has demonstrated more potential to breakthroughs in achieving state-of-the-art performance over public data sets. A recent paradigm that allows to work with trainable features is deep learning, which aims at learning features directly from raw image data. Although deep learning has recently achieved significant improvements in person re-identification, found on some few recent works, there is still room for learning strategies, which can be exploited to increase the current state-of-the-art performance. In this work a novel deep learning strategy is proposed, called here as coarse-to-fine learning (CFL), as well as a novel type of feature, called convolutional covariance features (CCF), for person re-identification. CFL is based on the human learning process. The core of CFL is a framework conceived to perform a cascade network training, learning person image features from generic-to-specific concepts about a person. Each network is comprised of a convolutional neural network (CNN) and a deep belief network denoising autoenconder (DBN-DAE). The CNN is responsible to learn local features, while the DBN-DAE learns global features, robust to illumination changing, certain image deformations, horizontal mirroring and image blurring. After extracting the convolutional features via CFL, those ones are then wrapped in covariance matrices, composing the CCF. CCF and flat features were combined to improve the performance of person re-identification in comparison with component features. The performance of the proposed framework was assessed comparatively against 18 state-of-the-art methods by using public data sets (VIPeR, i-LIDS, CUHK01 and CUHK03), cumulative matching characteristic curves and top ranking references. After a thorough analysis, our proposed framework demonstrated a superior performance.

Automated navigability assessment based on image sensor data is an important concern in the design of autonomous robotic systems. The problem consists in finding a mapping from input data to the navigability status of different areas of the surrounding world. Machine learning techniques are often applied to this problem. This thesis investigates an approach to navigability assessment in the image plane, based on offline learning using deep convolutional neural networks, applied to RGB and depth data collected using a robotic platform. Training outputs were generated by manually marking out instances of near collision in the sequences and tracing back the location of the near-collision frame through the previous frames. Several combinations of network inputs were tried out. Inputs included grayscale gradient versions of the RGB frames, depth maps, image coordinate maps and motion information in the form of a previous RGB frame or heading maps. Some improvement compared to simple depth thresholding was demonstrated, mainly in the handling of noise and missing pixels in the depth maps. The resulting networks appear to be mostly dependent on depth information; an attempt to train a network without the depth frames was unsuccessful,and a network trained using the depth frames alone performed similarly to networks trained with additional inputs. An unsuccessful attempt at training a network towards a more motion-dependent navigability concept was also made. It was done by including training frames captured as the robot was moving away from the obstacle, where the corresponding training outputs were marked as obstacle-free.