Post-Westgate SWAT : C4ISTAR Architectural Framework for Autonomous Network Integrated Multifaceted Warfighting Solutions Version 1.0 : A Peer-Reviewed Monograph

Police SWAT teams and Military Special Forces face mounting pressure and challenges from adversaries that can only be resolved by way of ever more sophisticated inputs into tactical operations. Lethal Autonomy provides constrained military/security forces with a viable option, but only if implementation has got proper empirically supported foundations. Autonomous weapon systems can be designed and developed to conduct ground, air and naval operations. This monograph offers some insights into the challenges of developing legal, reliable and ethical forms of autonomous weapons, that address the gap between Police or Law Enforcement and Military operations that is growing exponentially small. National adversaries are today in many instances hybrid threats, that manifest criminal and military traits, these often require deployment of hybrid-capability autonomous weapons imbued with the capability to taken on both Military and/or Security objectives. The Westgate Terrorist Attack of 21st September 2013 in the Westlands suburb of Nairobi, Kenya is a very clear manifestation of the hybrid combat scenario that required military response and police investigations against a fighting cell of the Somalia based globally networked Al Shabaab terrorist group.Comment: 52 pages, 6 Figures, over 40 references, reviewed by a reade

Environment and task modeling of long-term-autonomous service robots

Utilizing service robots in real-world tasks can significantly improve efficiency, productivity, and safety in various fields such as healthcare, hospitality, and transportation. However, integrating these robots into complex, human-populated environments for continuous use is a significant challenge. A key potential for addressing this challenge lies in long-term modeling capabilities to navigate, understand, and proactively exploit these environments for increased safety and better task performance. For example, robots may use this long-term knowledge of human activity to avoid crowded spaces when navigating or improve their human-centric services. This thesis proposes comprehensive approaches to improve the mapping, localization, and task fulfillment capabilities of service robots by leveraging multi-modal sensor information and (long- term) environment modeling. Learned environmental dynamics are actively exploited to improve the task performance of service robots. As a first contribution, a new long-term-autonomous service robot is presented, designed for both inside and outside buildings. The multi-modal sensor information provided by the robot forms the basis for subsequent methods to model human-centric environments and human activity. It is shown that utilizing multi-modal data for localization and mapping improves long-term robustness and map quality. This especially applies to environments of varying types, i.e., mixed indoor and outdoor or small-scale and large-scale areas. Another essential contribution is a regression model for spatio-temporal prediction of human activity. The model is based on long-term observations of humans by a mobile robot. It is demonstrated that the proposed model can effectively represent the distribution of detected people resulting from moving robots and enables proactive navigation planning. Such model predictions are then used to adapt the robot’s behavior by synthesizing a modular task control model. A reactive executive system based on behavior trees is introduced, which actively triggers recovery behaviors in the event of faults to improve the long-term autonomy. By explicitly addressing failures of robot software components and more advanced problems, it is shown that errors can be solved and potential human helpers can be found efficiently

Dependable System Design for Assistance Systems for Electrically Powered Wheelchairs

In this paper a system design approach is proposed, which is based on a user needs assessment and a flexible and adaptable architecture for dependable system integration. The feasibility of the approach is shown on the example of an assistance system for electrically powered wheelchairs. The system requirements correspond to the cognitive and motor abilities of the wheelchair users. For the wheelchair system built up based on a commercial powered wheelchair several behaviors have been realized such as collision avoidance, local navigation and path planning well known from robotic systems, which are enhanced by human-interfacing components. Furthermore, the system design will be high lighted which is based on robotic systems engineering. Due to the fundamental properties of the system architecture the resulting assistance system is inherently dependable, flexible, and adaptable. Corresponding to the current situation and the users’ abilities the system changes the level of assistance during real-time operation. The resulting system behavior is evaluated using system performance and usability tests

Multi-Agent Information Processing and Adaptive Control in Global Telecommunication and Computer Networks

The problems and methods for adaptive control and multi-agent processing of information in global telecommunication and computer networks (TCN) are discussed. Criteria for controllability and communication ability (routing ability) of dataflows are described. Multi-agent model for exchange of divided information resources in global TCN has been suggested. Peculiarities for adaptive and intelligent control of dataflows in uncertain conditions and network collisions are analyzed

Predictive Analytics Lead to Smarter Self-Organizing Directional Wireless Backbone Networks

Directional wireless systems are becoming a cost-effective approach towards providing a high-speed, reliable, broadband connection for the ubiquitous mobile wireless devices in use today. The most common of these systems consists of narrow-beam radio frequency (RF) and free-space-optical (FSO) links, which offer speeds between 100Mbps and 100Gbps while offering bit-error-rates comparable to fixed fiber optic installations. In addition, spatial and spectral efficiencies are accessible with directional wireless systems that cannot be matched with broadcast systems. The added benefits of compact designs permit the installation of directional antennas on-board unmanned autonomous systems (UAS) to provide network availability to regions prone to natural disasters, in maritime situations, and in war-torn countries that lack infrastructure security. In addition, through the use of intelligent network-centric algorithms, a flexible airborne backbone network can be established to dodge the scalability limitations of traditional omnidirectional wireless networks. Assuring end-to-end connectivity and coverage is the main challenge in the design of directional wireless backbone (DWB) networks. Conflating the duality of these objectives with the dynamical nature of the environment in which DWB networks are deployed, in addition to the standardized network metrics such as latency-minimization and throughput maximization, demands a rigorous control process that encompasses all aspects of the system. This includes the mechanical steering of the directional point-to-point link and the monitoring of aggregate network performance (e.g. dropped packets). The inclusion of processes for topology control, mobility management, pointing, acquisition, and tracking of the directional antennas, alongside traditional protocols (e.g. IPv6) provides a rigorous framework for next-generation mobile directional communication networks. This dissertation provides a novel approach to increase reliability in reconfigurable beam-steered directional wireless backbone networks by predicating optimal network reconfigurations wherein the network is modeled as a giant molecule in which the point-to-point links between two UASs are able to grow and retract analogously to the bonds between atoms in a molecule. This cross-disciplinary methodology explores the application of potential energy surfaces and normal mode analysis as an extension to the topology control optimization. Each of these methodologies provides a new and unique ability for predicting unstable configurations of DWB networks through an understanding of second-order principle dynamics inherent within the aggregate configuration of the system. This insight is not available through monitoring individual link performance. Together, the techniques used to model the DWB network through molecular dynamics are referred to as predictive analytics and provide reliable results that lead to smarter self-organizing reconfigurable beam-steered DWB networks. Furthermore, a comprehensive control architecture is proposed that complements traditional network science (e.g. Internet protocol) and the unique design aspects of DWB networks. The distinct ability of a beam-steered DWB network to adjust the direction of its antennas (i.e. reconfigure) in response to degraded effects within the atmosphere or due to an increased separation of nodes, is not incorporated in traditional network processes such re-routing mechanism, and therefore, processes for reconfiguration can be abstracted which both optimize the physical interconnections while maintaining interoperability with existing protocols. This control framework is validated using network metrics for latency and throughput and compared to existing architectures which use only standard re-routing mechanisms. Results are shown that validate both the analogous molecular modeling of a reconfigurable beam-steered directional wireless backbone network and a comprehensive control architecture which coalesces the unique capabilities of reconfiguration and mobility of mobile wireless backbone networks with existing protocols for networks such as IPv6

DEVELOPMENT OF AN AUTONOMOUS NAVIGATION SYSTEM FOR THE SHUTTLE CAR IN UNDERGROUND ROOM & PILLAR COAL MINES

In recent years, autonomous solutions in the multi-disciplinary field of the mining engineering have been an extremely popular applied research topic. The growing demand for mineral supplies combined with the steady decline in the available surface reserves has driven the mining industry to mine deeper underground deposits. These deposits are difficult to access, and the environment may be hazardous to mine personnel (e.g., increased heat, difficult ventilation conditions, etc.). Moreover, current mining methods expose the miners to numerous occupational hazards such as working in the proximity of heavy mining equipment, possible roof falls, as well as noise and dust. As a result, the mining industry, in its efforts to modernize and advance its methods and techniques, is one of the many industries that has turned to autonomous systems. Vehicle automation in such complex working environments can play a critical role in improving worker safety and mine productivity. One of the most time-consuming tasks of the mining cycle is the transportation of the extracted ore from the face to the main haulage facility or to surface processing facilities. Although conveyor belts have long been the autonomous transportation means of choice, there are still many cases where a discrete transportation system is needed to transport materials from the face to the main haulage system. The current dissertation presents the development of a navigation system for an autonomous shuttle car (ASC) in underground room and pillar coal mines. By introducing autonomous shuttle cars, the operator can be relocated from the dusty, noisy, and potentially dangerous environment of the underground mine to the safer location of a control room. This dissertation focuses on the development and testing of an autonomous navigation system for an underground room and pillar coal mine. A simplified relative localization system which determines the location of the vehicle relatively to salient features derived from on-board 2D LiDAR scans was developed for a semi-autonomous laboratory-scale shuttle car prototype. This simplified relative localization system is heavily dependent on and at the same time leverages the room and pillar geometry. Instead of keeping track of a global position of the vehicle relatively to a fixed coordinates frame, the proposed custom localization technique requires information regarding only the immediate surroundings. The followed approach enables the prototype to navigate around the pillars in real-time using a deterministic Finite-State Machine which models the behavior of the vehicle in the room and pillar mine with only a few states. Also, a user centered GUI has been developed that allows for a human user to control and monitor the autonomous vehicle by implementing the proposed navigation system. Experimental tests have been conducted in a mock mine in order to evaluate the performance of the developed system. A number of different scenarios simulating common missions that a shuttle car needs to undertake in a room and pillar mine. The results show a minimum success ratio of 70%

Risk analysis of autonomous vehicle and its safety impact on mixed traffic stream

In 2016, more than 35,000 people died in traffic crashes, and human error was the reason for 94% of these deaths. Researchers and automobile companies are testing autonomous vehicles in mixed traffic streams to eliminate human error by removing the human driver behind the steering wheel. However, recent autonomous vehicle crashes while testing indicate the necessity for a more thorough risk analysis. The objectives of this study were (1) to perform a risk analysis of autonomous vehicles and (2) to evaluate the safety impact of these vehicles in a mixed traffic stream. The overall research was divided into two phases: (1) risk analysis and (2) simulation of autonomous vehicles. Risk analysis of autonomous vehicles was conducted using the fault tree method. Based on failure probabilities of system components, two fault tree models were developed and combined to predict overall system reliability. It was found that an autonomous vehicle system could fail 158 times per one-million miles of travel due to either malfunction in vehicular components or disruption from infrastructure components. The second phase of this research was the simulation of an autonomous vehicle, where change in crash frequency after autonomous vehicle deployment in a mixed traffic stream was assessed. It was found that average travel time could be reduced by about 50%, and 74% of conflicts, i.e., traffic crashes, could be avoided by replacing 90% of the human drivers with autonomous vehicles