Graceful Navigation for Mobile Robots in Dynamic and Uncertain Environments.

The ability to navigate in everyday environments is a fundamental and necessary skill for any autonomous mobile agent that is intended to work with human users. The presence of pedestrians and other dynamic objects, however, makes the environment inherently dynamic and uncertain. To navigate in such environments, an agent must reason about the near future and make an optimal decision at each time step so that it can move safely toward the goal. Furthermore, for any application intended to carry passengers, it also must be able to move smoothly and comfortably, and the robot behavior needs to be customizable to match the preference of the individual users. Despite decades of progress in the field of motion planning and control, this remains a difficult challenge with existing methods. In this dissertation, we show that safe, comfortable, and customizable mobile robot navigation in dynamic and uncertain environments can be achieved via stochastic model predictive control. We view the problem of navigation in dynamic and uncertain environments as a continuous decision making process, where an agent with short-term predictive capability reasons about its situation and makes an informed decision at each time step. The problem of robot navigation in dynamic and uncertain environments is formulated as an on-line, finite-horizon policy and trajectory optimization problem under uncertainty. With our formulation, planning and control becomes fully integrated, which allows direct optimization of the performance measure. Furthermore, with our approach the problem becomes easy to solve, which allows our algorithm to run in real time on a single core of a typical laptop with off-the-shelf optimization packages. The work presented in this thesis extends the state-of-the-art in analytic control of mobile robots, sampling-based optimal path planning, and stochastic model predictive control. We believe that our work is a significant step toward safe and reliable autonomous navigation that is acceptable to human users.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120760/1/jongjinp_1.pd

A Person-Centric Design Framework for At-Home Motor Learning in Serious Games

abstract: In motor learning, real-time multi-modal feedback is a critical element in guided training. Serious games have been introduced as a platform for at-home motor training due to their highly interactive and multi-modal nature. This dissertation explores the design of a multimodal environment for at-home training in which an autonomous system observes and guides the user in the place of a live trainer, providing real-time assessment, feedback and difficulty adaptation as the subject masters a motor skill. After an in-depth review of the latest solutions in this field, this dissertation proposes a person-centric approach to the design of this environment, in contrast to the standard techniques implemented in related work, to address many of the limitations of these approaches. The unique advantages and restrictions of this approach are presented in the form of a case study in which a system entitled the "Autonomous Training Assistant" consisting of both hardware and software for guided at-home motor learning is designed and adapted for a specific individual and trainer. In this work, the design of an autonomous motor learning environment is approached from three areas: motor assessment, multimodal feedback, and serious game design. For motor assessment, a 3-dimensional assessment framework is proposed which comprises of 2 spatial (posture, progression) and 1 temporal (pacing) domains of real-time motor assessment. For multimodal feedback, a rod-shaped device called the "Intelligent Stick" is combined with an audio-visual interface to provide feedback to the subject in three domains (audio, visual, haptic). Feedback domains are mapped to modalities and feedback is provided whenever the user's performance deviates from the ideal performance level by an adaptive threshold. Approaches for multi-modal integration and feedback fading are discussed. Finally, a novel approach for stealth adaptation in serious game design is presented. This approach allows serious games to incorporate motor tasks in a more natural way, facilitating self-assessment by the subject. An evaluation of three different stealth adaptation approaches are presented and evaluated using the flow-state ratio metric. The dissertation concludes with directions for future work in the integration of stealth adaptation techniques across the field of exergames.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Autonomous Navigation for Mobile Robots in Crowded Environments

L'abstract è presente nell'allegato / the abstract is in the attachmen

Development of an Emergent Narrative Generation Architecture for Videogames

Emergent narratives can enhance a video gaming experience. However, the use of this storytelling form in videogames is in its infancy. Many existing emergent narrative generation systems are limited in authorial creative control, story world flexibility, user freedom and/or general storyline maintenance. In this thesis, we designed a robust architecture for generating emergent narratives to use in videogames, and tested the architecture in a prototype game simulation. The architecture continuously presents pre-written plot fragments with fulfilled preconditions, providing authorial control over the story’s components and general direction. The user navigates the plotlines and shapes the story through their unrestrained decisions. Architectural components monitor these actions and respond appropriately to ensure a cohesive story. Yet, the narrative generation framework remains separate from specific Game World mechanics, maintaining compatibility with any story world. The proposed architecture offers flexible emergent narrative generation and can provide a framework for future emergent videogame development

Leveraging Connected Highway Vehicle Platooning Technology to Improve the Efficiency and Effectiveness of Train Fleeting Under Moving Blocks

Future advanced Positive Train Control systems may allow North American railroads to introduce moving blocks with shorter train headways. This research examines how closely following trains respond to different throttle and brake inputs. Using insights from connected automobile and truck platooning technology, six different following train control algorithms were developed, analyzed for stability, and evaluated with simulated fleets of freight trains. While moving blocks require additional train spacing beyond minimum safe braking distance to account for train control actions, certain following train algorithms can help minimize this distance and balance fuel efficiency and train headway by changing control parameters

Punctuated Multi-Layered Liminality in Digital Transformation: The Case of an Automotive Platform

Digital transformation is often characterized as a liminal process as organizations move from established practices to new ways of organizing afforded by digital technology. Two contrasting views exist, however, on the liminality of digital transformation. One view sees liminality as a discrete transient process, while the other sees it as an on-going continuous transition. Building on a case study around a digital innovation initiative of an incumbent automotive car manufacturer, we offer a third view. We find that digital innovation triggers a phase of punctuated, multi-layered liminality that has a material, structural and temporal layer. We explain how material, temporal and structural tensions unfold at the level of practice, triggering new forms of liminal practices. We further develop three mechanisms (boundary testing, temporal bridging, and structural recoupling) that underpin punctuated multi-layered liminality. We contribute by unpacking the relationship between digital innovation and digital transformation

Advances in Reinforcement Learning

Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic

Latest Trend in Person Following Robot Control Algorithm: A Review

Person Following Robot (PFR) is recently a very popular research for mobile robots. PFR is widely developed by many researchers and labs. Three main functions of the robot that needed to be considered to develop a Person Following Robot are hardware mechanism, tracking mechanism, and following control system. To make certain that the mobile robot able to follow the leader (human), the robot should be able to track the leader whether in front, side-by-side, or behind the robot. Most researches develop tracking system by using sensor fusion especially laser and vision sensor. After the mobile robot tracked the correct target, then following algorithm is designed to make the mobile robot follow the target. This is also known as robot control, where robot receives input of tracking data and output the movement of the robot accordingly. There are various methods of control algorithm, from the simplest trajectory following algorithm to a highly complex behavior based model. This paper covers the review of the latest trend in person following robot control algorithm

Aerospace medicine and biology. A continuing bibliography (supplement 231)

This bibliography lists 284 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1982