Robot motion planning using real-time heuristic search

Autonomous mobile robots must be able to plan quickly and stay reactive to the world around them. Currently, navigating in the presence of dynamic obstacles is a problem that modern techniques struggle to handle in a real-time manner, even when the environment is known. The solutions range from using: 1) sampling-based algorithms which cut down on the shear size of these state spaces, 2) algorithms which quickly try to plan complete paths to the goal (to avoid local minima) and 3) using real-time search techniques designed for static worlds. Each of these methods have fundamental flaws that prevent it from being used in practice. In this thesis I offer three proposed techniques to help improve planning among dynamic obstacles. First, I present a new partitioned learning technique for splitting the costs estimates used by heuristic search techniques into those caused by the static environment and those caused by the dynamic obstacles in the world. This allows for much more accurate learning. Second, I introduce a novel decaying heuristic technique for generalizing cost-to-go over states of the same pose (x. y.theta.v) in the world. Third, I show a garbage collection mechanism for removing useless states from our search to cut down on the overall memory usage. Finally, I present a new algorithm called Partitioned Learning Real-time A*. PLRTA* uses all three of these new enhancements to navigate through worlds with dynamic obstacles in a real-time manner while handling the complex situations in which other algorithms fail. I empirically compare our algorithm to other competing algorithms in a number of random instances as well as hand crafted scenarios designed to highlight desirable behavior in specific situations. I show that PLRTA* outperforms the current state-of-the-art algorithms in terms of minimizing cost over a large number of robot motion planning problems, even when planning in fairly confined environments with up to ten dynamic obstacles

Towards a Scalable Dynamic Spatial Database System

With the rise of GPS-enabled smartphones and other similar mobile devices, massive amounts of location data are available. However, no scalable solutions for soft real-time spatial queries on large sets of moving objects have yet emerged. In this paper we explore and measure the limits of actual algorithms and implementations regarding different application scenarios. And finally we propose a novel distributed architecture to solve the scalability issues.Comment: (2012

Real-time motion data annotation via action string

Even though there is an explosive growth of motion capture data, there is still a lack of efficient and reliable methods to automatically annotate all the motions in a database. Moreover, because of the popularity of mocap devices in home entertainment systems, real-time human motion annotation or recognition becomes more and more imperative. This paper presents a new motion annotation method that achieves both the aforementioned two targets at the same time. It uses a probabilistic pose feature based on the Gaussian Mixture Model to represent each pose. After training a clustered pose feature model, a motion clip could be represented as an action string. Then, a dynamic programming-based string matching method is introduced to compare the differences between action strings. Finally, in order to achieve the real-time target, we construct a hierarchical action string structure to quickly label each given action string. The experimental results demonstrate the efficacy and efficiency of our method

Investigating affordances of virtual worlds for real world B2C e-commerce

Virtual worlds are three-dimensional (3D) online persistent multi-user environments where users interact through avatars. The literature suggests that virtual worlds can facilitate real world business-to-consumer (B2C) e-commerce. However, few real world businesses have adopted virtual worlds for B2C e-commerce. In this paper, we present results from interviews with consumers in a virtual world to investigate how virtual worlds can support B2C e-commerce. A thematic analysis of the data was conducted to uncover affordances and constraints of virtual worlds for B2C e-commerce. Two affordances (habitability and appearance of realness) and one constraint (demand for specialised skill) were uncovered. The implications of this research for designers are (1) to provide options to consumers that enable them to manage their online reputation, (2) to focus on managing consumers’ expectations and (3) to facilitate learning between consumers

Serious Games in Cultural Heritage

Although the widespread use of gaming for leisure purposes has been well documented, the use of games to support cultural heritage purposes, such as historical teaching and learning, or for enhancing museum visits, has been less well considered. The state-of-the-art in serious game technology is identical to that of the state-of-the-art in entertainment games technology. As a result the field of serious heritage games concerns itself with recent advances in computer games, real-time computer graphics, virtual and augmented reality and artificial intelligence. On the other hand, the main strengths of serious gaming applications may be generalised as being in the areas of communication, visual expression of information, collaboration mechanisms, interactivity and entertainment. In this report, we will focus on the state-of-the-art with respect to the theories, methods and technologies used in serious heritage games. We provide an overview of existing literature of relevance to the domain, discuss the strengths and weaknesses of the described methods and point out unsolved problems and challenges. In addition, several case studies illustrating the application of methods and technologies used in cultural heritage are presented

Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs

In this paper, we present Batch Informed Trees (BIT*), a planning algorithm based on unifying graph- and sampling-based planning techniques. By recognizing that a set of samples describes an implicit random geometric graph (RGG), we are able to combine the efficient ordered nature of graph-based techniques, such as A*, with the anytime scalability of sampling-based algorithms, such as Rapidly-exploring Random Trees (RRT). BIT* uses a heuristic to efficiently search a series of increasingly dense implicit RGGs while reusing previous information. It can be viewed as an extension of incremental graph-search techniques, such as Lifelong Planning A* (LPA*), to continuous problem domains as well as a generalization of existing sampling-based optimal planners. It is shown that it is probabilistically complete and asymptotically optimal. We demonstrate the utility of BIT* on simulated random worlds in $\mathbb{R}^2$ and $\mathbb{R}^8$ and manipulation problems on CMU's HERB, a 14-DOF two-armed robot. On these problems, BIT* finds better solutions faster than RRT, RRT*, Informed RRT*, and Fast Marching Trees (FMT*) with faster anytime convergence towards the optimum, especially in high dimensions.Comment: 8 Pages. 6 Figures. Video available at http://www.youtube.com/watch?v=TQIoCC48gp

Developing serious games for cultural heritage: a state-of-the-art review

Although the widespread use of gaming for leisure purposes has been well documented, the use of games to support cultural heritage purposes, such as historical teaching and learning, or for enhancing museum visits, has been less well considered. The state-of-the-art in serious game technology is identical to that of the state-of-the-art in entertainment games technology. As a result, the field of serious heritage games concerns itself with recent advances in computer games, real-time computer graphics, virtual and augmented reality and artificial intelligence. On the other hand, the main strengths of serious gaming applications may be generalised as being in the areas of communication, visual expression of information, collaboration mechanisms, interactivity and entertainment. In this report, we will focus on the state-of-the-art with respect to the theories, methods and technologies used in serious heritage games. We provide an overview of existing literature of relevance to the domain, discuss the strengths and weaknesses of the described methods and point out unsolved problems and challenges. In addition, several case studies illustrating the application of methods and technologies used in cultural heritage are presented

Small-World File-Sharing Communities

Web caches, content distribution networks, peer-to-peer file sharing networks, distributed file systems, and data grids all have in common that they involve a community of users who generate requests for shared data. In each case, overall system performance can be improved significantly if we can first identify and then exploit interesting structure within a community's access patterns. To this end, we propose a novel perspective on file sharing based on the study of the relationships that form among users based on the files in which they are interested. We propose a new structure that captures common user interests in data--the data-sharing graph-- and justify its utility with studies on three data-distribution systems: a high-energy physics collaboration, the Web, and the Kazaa peer-to-peer network. We find small-world patterns in the data-sharing graphs of all three communities. We analyze these graphs and propose some probable causes for these emergent small-world patterns. The significance of small-world patterns is twofold: it provides a rigorous support to intuition and, perhaps most importantly, it suggests ways to design mechanisms that exploit these naturally emerging patterns