A Bayesian hierarchy for robust gaze estimation in human–robot interaction

In this text, we present a probabilistic solution for robust gaze estimation in the context of human–robot interaction. Gaze estimation, in the sense of continuously assessing gaze direction of an interlocutor so as to determine his/her focus of visual attention, is important in several important computer vision applications, such as the development of non-intrusive gaze-tracking equipment for psychophysical experiments in neuroscience, specialised telecommunication devices, video surveillance, human–computer interfaces (HCI) and artificial cognitive systems for human–robot interaction (HRI), our application of interest. We have developed a robust solution based on a probabilistic approach that inherently deals with the uncertainty of sensor models, but also and in particular with uncertainty arising from distance, incomplete data and scene dynamics. This solution comprises a hierarchical formulation in the form of a mixture model that loosely follows how geometrical cues provided by facial features are believed to be used by the human perceptual system for gaze estimation. A quantitative analysis of the proposed framework's performance was undertaken through a thorough set of experimental sessions. Results show that the framework performs according to the difficult requirements of HRI applications, namely by exhibiting correctness, robustness and adaptiveness

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

An approach to reducing parameter uncertainty for robotic surface assembly tasks

In contrast to hard automation, which relies on the precise knowledge of all parameters and special-purpose machinery, the goal of flexible assembly is to overcome the inherent uncertainty in the location of the parts. The main result of this dissertation is that, for rigid, non-deformable objects, more accurate estimates of parameters, which describe their position and orientation in Cartesian space, can be obtained through active part interaction and estimation using numerical methods. If the objects have large polyhedral or convex features, the parameter estimation problem can be recasting terms of fitting the collected empirical data to a suitable geometrical model. The planning and execution steps are treated as conceptually separate from the estimation.Additionally, an algorithm for automatic conversion of a compliant path from the Cartesian to the joint space of a general-purpose, 7 degrees of freedom robotic arm is described. This allows for the assembly strategies to be planned in terms of objects'topological features in the task frame. A `back-drivable' Barrett WAM robotic arm without a force sensor was used in all experiments, and approximate compliant motion was achieved by relying on torque limits and impedance. Consequently, the primary focus is on planning, control, and assembly without force sensing. The underlying concepts, however, are much more general and could be extended to incorporate force feedback. Grasping is outside of the scope of this work, and it is assumed throughout that one of the parts is rigidly attached to the end-effector of the robot

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Predictive Maneuver Planning and Control of an Autonomous Vehicle in Multi-Vehicle Traffic with Observation Uncertainty

Autonomous vehicle technology is a promising development for improving the safety, efficiency and environmental impact of on-road transportation systems. However, the task of guiding an autonomous vehicle by rapidly and systematically accommodating the plethora of changing constraints, e.g. of avoiding multiple stationary and moving obstacles, obeying traffic rules, signals and so on as well as the uncertain state observation due to sensor imperfections, remains a major challenge. This dissertation attempts to address this challenge via designing a robust and efficient predictive motion planning framework that can generate the appropriate vehicle maneuvers (selecting and tracking specific lanes, and related speed references) as well as the constituent motion trajectories while considering the differential vehicle kinematics of the controlled vehicle and other constraints of operating in public traffic. The main framework combines a finite state machine (FSM)-based maneuver decision module with a model predictive control (MPC)-based trajectory planner. Based on the prediction of the traffic environment, reference speeds are assigned to each lane in accordance with the detection of objects during measurement update. The lane selection decisions themselves are then incorporated within the MPC optimization. The on-line maneuver/motion planning effort for autonomous vehicles in public traffic is a non-convex problem due to the multiple collision avoidance constraints with overlapping areas, lane boundaries, and nonlinear vehicle-road dynamics constraints. This dissertation proposes and derives some remedies for these challenges within the planning framework to improve the feasibility and optimality of the solution. Specifically, it introduces vehicle grouping notions and derives conservative and smooth algebraic models to describe the overlapped space of several individual infeasible spaces and help prevent the optimization from falling into undesired local minima. Furthermore, in certain situations, a forced objective selection strategy is needed and adopted to help the optimization jump out of local minima. Furthermore, the dissertation considers stochastic uncertainties prevalent in dynamic and complex traffic and incorporate them with in the predictive planning and control framework. To this end, Bayesian filters are implemented to estimate the uncertainties in object motions and then propagate them into the prediction horizon. Then, a pair-wise probabilistic collision condition is defined for objects with non-negligible geometrical shape/sizes and computationally efficient and conservative forms are derived to efficiently and analytically approximate the involved multi-variate integrals. The probabilistic collision evaluation is then applied within a vehicle grouping algorithms to cluster the object vehicles with closeness in positions and speeds and eventually within the stochastic predictive maneuver planner framework to tighten the chanced-constraints given a deterministic confidence margin. It is argued that these steps make the planning problem tractable for real-time implementation on autonomously controlled vehicles

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition