Autonomous robot patrolling of a sparsely populated unknown environment

The increasing availability and affordability of autonomous robots has expanded their uses for many new applications, such as exploration, surveillance and threat containment. Most research considers a team of a large number of robots that contain global information. This work explores distributed and low overhead algorithms for patrolling and threat containment within a region sparsely populated with few robots. The robots patrol the area without the global knowledge of the region, but each is equipped with an omni-directional range finder and a positioning system for keeping track of its location and metering distance and directions of events. This study presents the extent of effectiveness and limitations of utilizing a limited number of robots patrolling an unknown wide-spread region. A set of three algorithms was developed. All algorithms assume the use of artificial potential fields (APFs) for collision avoidance with other robots and the walls as well as to approach the threat. The algorithms differ in two ways; whether or not the robots have a limited memory of past events and the way the robots maneuver from one patrol target location to another. The next patrol target location can be derived randomly or based on past events. The past events include previously sensed robot locations, target locations, and walls. The algorithms are analyzed in terms of the time it takes for the robots to detect and neutralize threats within the surveillance region. Simulations via MATLAB are conducted to investigate the tradeoffs due to factors such as the number of robots, the size of the region, and the frequency of threats. The results show that the three algorithms perform comparably on average, achieving reasonable effectiveness given the inherent limitations that are lacking in the global information about the environment

ONLINE LEARNING WITH BANDITS FOR COVERAGE

With the rapid growth in velocity and volume, streaming data compels decision support systems to predict a small number of unique data points in due time that can represent a massive amount of correlated data without much loss of precision. In this work, we formulate this problem as the {\it online set coverage problem} and propose its solution for recommendation systems and the patrol assignment problem. We propose a novel online reinforcement learning algorithm inspired by the Multi-Armed Bandit problem to solve the online recommendation system problem. We introduce a graph-based mechanism to improve the user coverage by recommended items and show that the mechanism can facilitate the coordination between bandits and therefore, reduce the overall complexity. Our graph-based bandit algorithm can select a much smaller set of items to cover a vast variety of users’ choices for recommendation systems. We present our experimental results in a partially observable real-world environment. We also study the patrol assignment as an online set coverage problem, which presents an additional level of difficulty. Along with covering the susceptible routes by learning the diversity of attacks, unlike in recommendation systems, our technique needs to make choices against actively engaging adversarial opponents. We assume that attacks over those routes are posed by intelligent entities, capable of reacting with their best responses. Therefore, to model such attacks, we used the Stackelberg Security Game. We augment our graph-based bandit defenders with adaptive adjustment of reward coming from this game to perplex the attackers and gradually succeed over them by maximizing the confrontation. We found that our graph bandits can outperform other Multi-Arm bandit algorithms when a simulated annealing-based scheduling is incorporated to adjust the balance between exploration and exploitation

Robotic Searching for Stationary, Unknown and Transient Radio Sources

Searching for objects in physical space is one of the most important tasks for humans. Mobile sensor networks can be great tools for the task. Transient targets refer to a class of objects which are not identifiable unless momentary sensing and signaling conditions are satisfied. The transient property is often introduced by target attributes, privacy concerns, environment constraints, and sensing limitations. Transient target localization problems are challenging because the transient property is often coupled with factors such as sensing range limits, various coverage functions, constrained mobility, signal correspondence, limited number of searchers, and a vast searching region. To tackle these challenge tasks, we gradually increase complexity of the transient target localization problem such as Single Robot Single Target (SRST), Multiple Robots Single Target (MRST), Single Robot Multiple Targets (SRMT) and Multiple Robots Multiple Targets (MRMT). We propose the expected searching time (EST) as a primary metric to assess the searching ability of a single robot and the spatiotemporal probability occupancy grid (SPOG) method that captures transient characteristics of multiple targets and tracks the spatiotemporal posterior probability distribution of the target transmissions. Besides, we introduce a team of multiple robots and develop a sensor fusion model using the signal strength ratio from the paired robots in centralized and decentralized manners. We have implemented and validated the algorithms under a hardware-driven simulation and physical experiments

Overview of Key Technologies for Water-based Automatic Security Marking Platform

Water-based automatic security marking platform composed of multifunctional underwater robots and unmanned surface vessel has become the development trend and focus for exploring complex and dangerous waters,and its related technologies have flourished and gradually developed from single control to multi-platform collaborative direction in complex and dangerous waters to reduce casualties. This paper composes and analyzes the key technologies of the water-based automatic security marking platform based on the cable underwater robot and the unmanned surface vessel, describes the research and application status of the key technologies of the water-based automatic security marking platform from the aspects of the unmanned surface vessel, underwater robot and underwater multisensor information fusion, and outlooks the research direction and focus of the water automatic security inspection and marking platform

Adaptive Cognitive Agents: Updating Action Descriptions and Plans

In this paper we present an extension of Belief-Desire-Intention agents which can adapt their performance in response to changes in their environment. We consider situations in which the agent’s actions no longer perform as anticipated. Our agents maintain explicit descriptions of the expected behaviour of their actions, are able to track action performance, learn new action descriptions and patch affected plans at runtime. Our main contributions are the underlying theoretical mechanisms for data collection about action performance, the synthesis of new action descriptions from this data and the integration with plan reconfiguration. The mechanisms are supported by a practical implementation to validate the approach.<br/

Intrusion Detection Mechanism for Empowered Intruders Using IDEI

In the past, intrusion detection has been extensively investigated as a means of ensuring the security of wireless sensor networks. Anti-recon technology has made it possible for an attacker to get knowledge about the detecting nodes and plot a route around them in order to evade detection. An "empowered intruder" is one who poses new threats to current intrusion detection technologies. Furthermore, the intended impact of detection may not be obtained in certain subareas owing to gaps in coverage caused by the initial deployment of detection nodes at random. A vehicle collaboration sensing network model is proposed to solve these difficulties, in which mobile sensing cars and static sensor nodes work together to identify intrusions by empowered intruders. An algorithm for mobile sensing vehicles, called Intrusion Detection Mechanism for Empowered Intruders(IDEI), and a sleep-scheduling technique for static nodes form the basis of our proposal. Sophisticated intruders will be tracked by mobile sensors, which will fill in the gaps in coverage, while static nodes follow a sleep schedule and will be woken when the intruder is discovered close. Our solution is compared to current techniques like Kinetic Theory Based Mobile Sensor Network (KMsn)and Mean Time to Attacks (MTTA) in terms of intrusion detection performance, energy usage, and sensor node movement distance. IDEI's parameter sensitivity is also examined via comprehensive simulations. It is clear from the theoretical analysis and simulation findings that our idea is more efficient and available

Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns

We introduce Deep Thermal Imaging, a new approach for close-range automatic recognition of materials to enhance the understanding of people and ubiquitous technologies of their proximal environment. Our approach uses a low-cost mobile thermal camera integrated into a smartphone to capture thermal textures. A deep neural network classifies these textures into material types. This approach works effectively without the need for ambient light sources or direct contact with materials. Furthermore, the use of a deep learning network removes the need to handcraft the set of features for different materials. We evaluated the performance of the system by training it to recognise 32 material types in both indoor and outdoor environments. Our approach produced recognition accuracies above 98% in 14,860 images of 15 indoor materials and above 89% in 26,584 images of 17 outdoor materials. We conclude by discussing its potentials for real-time use in HCI applications and future directions.Comment: Proceedings of the 2018 CHI Conference on Human Factors in Computing System

AI for the Common Good?! Pitfalls, challenges, and Ethics Pen-Testing

Recently, many AI researchers and practitioners have embarked on research visions that involve doing AI for "Good". This is part of a general drive towards infusing AI research and practice with ethical thinking. One frequent theme in current ethical guidelines is the requirement that AI be good for all, or: contribute to the Common Good. But what is the Common Good, and is it enough to want to be good? Via four lead questions, I will illustrate challenges and pitfalls when determining, from an AI point of view, what the Common Good is and how it can be enhanced by AI. The questions are: What is the problem / What is a problem?, Who defines the problem?, What is the role of knowledge?, and What are important side effects and dynamics? The illustration will use an example from the domain of "AI for Social Good", more specifically "Data Science for Social Good". Even if the importance of these questions may be known at an abstract level, they do not get asked sufficiently in practice, as shown by an exploratory study of 99 contributions to recent conferences in the field. Turning these challenges and pitfalls into a positive recommendation, as a conclusion I will draw on another characteristic of computer-science thinking and practice to make these impediments visible and attenuate them: "attacks" as a method for improving design. This results in the proposal of ethics pen-testing as a method for helping AI designs to better contribute to the Common Good.Comment: to appear in Paladyn. Journal of Behavioral Robotics; accepted on 27-10-201