Improving Occupancy Grid FastSLAM by Integrating Navigation Sensors

When an autonomous vehicle operates in an unknown environment, it must remember the locations of environmental objects and use those object to maintain an accurate location of itself. This vehicle is faced with Simultaneous Localization and Mapping (SLAM), a circularly defined robotics problem of map building with no prior knowledge. The SLAM problem is a difficult but critical component of autonomous vehicle exploration with applications to search and rescue missions. This paper presents the first SLAM solution combining stereo cameras, inertial measurements, and vehicle odometry into a Multiple Integrated Navigation Sensor (MINS) path. The FastSLAM algorithm, modified to make use of the MINS path, observes and maps the environment with a LIDAR unit. The MINS FastSLAM algorithm closes a 140 meter loop with a path error that remains within 1 meter of surveyed truth. This path reduces the error 79% from an odometry FastSLAM output and uses 30% of the particles

Cooperative Reinforcement Learning Using an Expert-Measuring Weighted Strategy with WoLF

Gradient descent learning algorithms have proven effective in solving mixed strategy games. The policy hill climbing (PHC) variants of WoLF (Win or Learn Fast) and PDWoLF (Policy Dynamics based WoLF) have both shown rapid convergence to equilibrium solutions by increasing the accuracy of their gradient parameters over standard Q-learning. Likewise, cooperative learning techniques using weighted strategy sharing (WSS) and expertness measurements improve agent performance when multiple agents are solving a common goal. By combining these cooperative techniques with fast gradient descent learning, an agent’s performance converges to a solution at an even faster rate. This statement is verified in a stochastic grid world environment using a limited visibility hunter-prey model with random and intelligent prey. Among five different expertness measurements, cooperative learning using each PHC algorithm converges faster than independent learning when agents strictly learn from better performing agents

CyberCraft: Protecting Electronic Systems with Lightweight Agents

The United States military is seeking new and innovative methods for securing and maintaining its computing and network resources locally and world-wide. This document presents a work-in-progress research thrust toward building a system capable of meeting many of the US military’s network security and sustainment requirements. The system is based on a Distributed Multi-Agent System (DMAS), that is secure, small, and scalable to the large networks found in the military. It relies on a staged agent architecture capable of dynamic configuration to support changing mission environments. These agents are combined into Hierarchical Peer-to-Peer (HP2P) networks to provide scalable solutions. They employ Public Key Infrastructure (PKI) communications (with digital signatures), and support trust chain management concepts. This document, a work-in-progress, presents the motivation and current challenges in choosing a network communications architecture capable of supporting one million or more agents in a DMAS

Windows Driver Memory Analysis: A Reverse Engineering Methodology

In a digital forensics examination, the capture and analysis of volatile data provides significant information on the state of the computer at the time of seizure. Memory analysis is a premier method of discovering volatile digital forensic information. While much work has been done in extracting forensic artifacts from Windows kernel structures, less focus has been paid to extracting information from Windows drivers. There are two reasons for this: (1) source code for one version of the Windows kernel (but not associated drivers) is available for educational use and (2) drivers are generally called asynchronously and contain no exported functions. Therefore, finding the handful of driver functions of interest out of the thousands of candidates makes reverse code engineering problematic at best. Developing a methodology to minimize the effort of analyzing these drivers, finding the functions of interest, and extracting the data structures of interest is highly desirable. This paper provides two contributions. First, it describes a general methodology for reverse code engineering of Windows drivers memory structures. Second it applies the methodology to tcpip.sys, a Windows driver that controls network connectivity. The result is the extraction from tcpip.sys of the data structures needed to determine current network connections and listeners from the 32 and 64 bit versions of Windows Vista and Windows 7. Manipulation (DKOM), tcpip.sys, Windows 7, Windows Vista. 2000 MSC: 60, 490

Timing Mark Detection on Nuclear Detonation Video

During the 1950s and 1960s the United States conducted and filmed over 200 atmospheric nuclear tests establishing the foundations of atmospheric nuclear detonation behavior. Each explosion was documented with about 20 videos from three or four points of view. Synthesizing the videos into a 3D video will improve yield estimates and reduce error factors. The videos were captured at a nominal 2500 frames per second, but range from 2300-3100 frames per second during operation. In order to combine them into one 3D video, individual video frames need to be correlated in time with each other. When the videos were captured a timing system was used that shined light in a video every 5 milliseconds creating a small circle exposed in the frame. This paper investigates several method of extracting the timing from images in the cases when the timing marks are occluded and washed out, as well as when the films are exposed as expected. Results show an improvement over past techniques. For normal videos, occluded videos, and washed out videos, timing is detected with 99.3%, 77.3%, and 88.6% probability with a 2.6%, 11.3%, 5.9% false alarm rate, respectively

Context-Driven Image Annotation Using ImageNet

Image annotation research has demonstrated success on test data for focused domains. Unfortunately, extending these techniques to the broader topics found in real world data often results in poor performance. This paper proposes a novel approach that leverages WordNet and ImageNet capabilities to annotate images based on local text and image features. Signatures generated from ImageNet images based on WordNet synonymous sets are compared using Earth Mover\u27s Distance against the query image and used to rank order surrounding words by relevancy. The results demonstrate effective image annotation, producing higher accuracy and improved specificity over the ALIPR image annotation system. Abstract © AAAI

Genetic Evolution of Hierarchical Behavior Structures

The development of coherent and dynamic behaviors for mobile robots is an exceedingly complex endeavor ruled by task objectives, environmental dynamics and the interactions within the behavior structure. This paper discusses the use of genetic programming techniques and the unified behavior framework to develop effective control hierarchies using interchangeable behaviors and arbitration components. Given the number of possible variations provided by the framework, evolutionary programming is used to evolve the overall behavior design. Competitive evolution of the behavior population incrementally develops feasible solutions for the domain through competitive ranking. By developing and implementing many simple behaviors independently and then evolving a complex behavior structure suited to the domain, this approach allows for the reuse of elemental behaviors and eases the complexity of development for a given domain. Additionally, this approach has the ability to locate a behavior structure which a developer may not have previously considered, and whose ability exceeds expectations. The evolution of the behavior structure is demonstrated using agents in the Robocode environment, with the evolved structures performing up to 122 percent better than one crafted by an expert

Acquiring OS X File Handles through Forensic Memory Analysis

Memory analysis has become a critical capability in digital forensics because it provides insight into system state that cannot be fully represented through traditional media analysis. The volafox open source project has begun the work of structured memory analysis for OS X with support for a limited set of kernel structures. This paper addresses one memory analysis deficiency on OS X with the introduction of a new volafox module for parsing file handles associated with running processes. The developed module outputs information comparable to the UNIX lsof (list open files) command, which is used to validate the results

Fuzzy State Aggregation and Off-Policy Reinforcement Learning for Stochastic Environments

Reinforcement learning is one of the more attractive machine learning technologies, due to its unsupervised learning structure and ability to continually learn even as the environment it is operating in changes. This ability to learn in an unsupervised manner in a changing environment is applicable in complex domains through the use of function approximation of the domain’s policy. The function approximation presented here is that of fuzzy state aggregation. This article presents the use of fuzzy state aggregation with the current policy hill climbing methods of Win or Lose Fast (WoLF) and policy-dynamics based WoLF (PD-WoLF), exceeding the learning rate and performance of the combined fuzzy state aggregation and Q-learning reinforcement learning. Results of testing using the TileWorld domain demonstrate the policy hill climbing performs better than the existing Q-learning implementations