Autonomous systems for operations in critical environments

This paper proposes an environment devoted to simulate the use of autonomous systems in the context of space exploratory missions and operations; this research focuses on supporting engineering of autonomous systems and of their innovative artificial intelligences through interoperable simulation. The proposed approach enables also development of training and educational solutions for use of robots and autonomous systems in space critical environments. The paper addresses different application areas including robotic inventory and warehouse solutions, intelligent space guard systems, drones for supporting extravehicular activities and for managing accidents and health emergencies. The paper investigates the potential of autonomous systems as well as their capability to interoperate with other systems and with humans, especially in critical environments. Finally, the paper presents the existing researches for interoperable simulators devoted to address these challenging topics within Simulation Exploratory Experience initiative

Who's Got the Bridge? - Towards Safe, Robust Autonomous Operations at NASA Langley's Autonomy Incubator

NASA aeronautics research has made decades of contributions to aviation. Both aircraft and air traffic management (ATM) systems in use today contain NASA-developed and NASA sponsored technologies that improve safety and efficiency. Recent innovations in robotics and autonomy for automobiles and unmanned systems point to a future with increased personal mobility and access to transportation, including aviation. Automation and autonomous operations will transform the way we move people and goods. Achieving this mobility will require safe, robust, reliable operations for both the vehicle and the airspace and challenges to this inevitable future are being addressed now in government labs, universities, and industry. These challenges are the focus of NASA Langley Research Center's Autonomy Incubator whose R&D portfolio includes mission planning, trajectory and path planning, object detection and avoidance, object classification, sensor fusion, controls, machine learning, computer vision, human-machine teaming, geo-containment, open architecture design and development, as well as the test and evaluation environment that will be critical to prove system reliability and support certification. Safe autonomous operations will be enabled via onboard sensing and perception systems in both data-rich and data-deprived environments. Applied autonomy will enable safety, efficiency and unprecedented mobility as people and goods take to the skies tomorrow just as we do on the road today

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

Soft robotics for infrastructure protection

The paradigm change introduced by soft robotics is going to dramatically push forward the abilities of autonomous systems in the next future, enabling their applications in extremely challenging scenarios. The ability of soft robots to safely interact and adapt to the surroundings is key to operate in unstructured environments, where the autonomous agent has little or no knowledge about the world around it. A similar context occurs when critical infrastructures face threats or disruptions, for examples due to natural disasters or external attacks (physical or cyber). In this case, autonomous systems may be employed to respond to such emergencies and have to be able to deal with unforeseen physical conditions and uncertainties, where the mechanical interaction with the environment is not only inevitable but also desirable to successfully perform their tasks. In this perspective, I discuss applications of soft robots for the protection of infrastructures, including recent advances in pipelines inspection, rubble search and rescue, and soft aerial manipulation, and promising perspectives on operations in radioactive environments, underwater monitoring and space exploration

Fuzzy Logic Path Planning System for Collision Avoidance by an Autonomous Rover Vehicle

Systems already developed at JSC have shown the benefits of applying fuzzy logic control theory to space related operations. Four major issues are addressed that are associated with developing an autonomous collision avoidance subsystem within a path planning system designed for application in a remote, hostile environment that does not lend itself well to remote manipulation of the vehicle involved through Earth-based telecommunication. A good focus for this is unmanned exploration of the surface of Mars. The uncertainties involved indicate that robust approaches such as fuzzy logic control are particularly appropriate. The four major issues addressed are: (1) avoidance of a single fuzzy moving obstacle; (2) back off from a dead end in a static obstacle environment; (3) fusion of sensor data to detect obstacles; and (4) options for adaptive learning in a path planning system