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All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Cognitive Communications for NASA Space Systems

The growing complexity of spacecraft constellations, communication relay offerings, and mission architectures drives the need for the development of autonomous communication systems. NASA has traditionally launched single spacecraft missions that are served by the Space Communication and Navigation (SCaN) program. Operations on SCaN networks are typically scheduled weeks in advance, and often each asset serves a single user spacecraft at a time. Recent movement towards swarm missions could make the current approach unsustainable. Additionally, the integration of commercial communication service providers will substantially increase the data transfer options available to new missions. NASA science missions have found benefit in launching swarms of spacecraft, allowing coordinated simultaneous observations from different perspectives. Inter-spacecraft communication (mesh networking) is an enabler for this architecture, as are CubeSats that allow cost-effective provisioning of distributed mission assets. As more complex swarm missions launch, one challenge is coordinating communication within the swarm and choosing the appropriate mechanism for telemetry, tracking, control, and data services to and from Earth. Cognitive communications research conducted by SCaN aims to mitigate the increasing communication complexity for mission users by increasing the autonomy of links, networks, and service scheduling. By considering automation techniques including recent advances in artificial intelligence and machine learning, cognitive algorithms and related approaches enable increased mission science return, improved resource utilization for service provider networks, and resiliency in unpredictable or unplanned environments. The Cognitive Communications Project at the NASA Glenn Research Center develops applications of data-driven, non-deterministic methods to improve the autonomy of space communication. The project emphasizes development of decentralized space networks with artificial intelligence agents optimizing communication link throughput, data routing, and system-wide asset management. This paper discusses the objectives, approaches, and opportunities of the research to address growing needs of the space communications community.

Chelmins, David↗
NASA NTRS2005

An Approach for Autonomy: A Collaborative Communication Framework for Multi-Agent Systems

Research done during the last three years has studied the emersion properties of Complex Adaptive Systems (CAS). The deployment of Artificial Intelligence (AI) techniques applied to remote Unmanned Aerial Vehicles has led the author to investigate applications of CAS within the field of Autonomous Multi-Agent Systems. The core objective of current research efforts is focused on the simplicity of Intelligent Agents (IA) and the modeling of these agents within complex systems. This research effort looks at the communication, interaction, and adaptability of multi-agents as applied to complex systems control. The embodiment concept applied to robotics has application possibilities within multi-agent frameworks. A new framework for agent awareness within a virtual 3D world concept is possible where the vehicle is composed of collaborative agents. This approach has many possibilities for applications to complex systems. This paper describes the development of an approach to apply this virtual framework to the NASA Goddard Space Flight Center (GSFC) tetrahedron structure developed under the Autonomous Nano Technology Swarm (ANTS) program and the Super Miniaturized Addressable Reconfigurable Technology (SMART) architecture program. These projects represent an innovative set of novel concepts deploying adaptable, self-organizing structures composed of many tetrahedrons. This technology is pushing current applied Agents Concepts to new levels of requirements and adaptability.

Dufrene, Warren Russell, Jr.↗
NASA NTRS2002

ANTS: Applying A New Paradigm for Lunar and Planetary Exploration

ANTS (Autonomous Nano- Technology Swarm), a mission architecture consisting of a large (1000 member) swarm of picoclass (1 kg) totally autonomous spacecraft with both adaptable and evolvable heuristic systems, is being developed as a NASA advanced mission concept, and is here examined as a paradigm for lunar surface exploration. As the capacity and complexity of hardware and software, demands for bandwidth, and the sophistication of goals for lunar and planetary exploration have increased, greater cost constraints have led to fewer resources and thus, the need to operate spacecraft with less frequent human contact. At present, autonomous operation of spacecraft systems allows great capability of spacecraft to 'safe' themselves and survive when conditions threaten spacecraft safety. To further develop spacecraft capability, NASA is at the forefront of development of new mission architectures which involve the use of Intelligent Software Agents (ISAs), performing experiments in space and on the ground to advance deliberative and collaborative autonomous control techniques. Selected missions in current planning stages require small groups of spacecraft weighing tens, instead of hundreds, of kilograms to cooperate at a tactical level to select and schedule measurements to be made by appropriate instruments onboard. Such missions will be characterizing rapidly unfolding real-time events on a routine basis. The next level of development, which we are considering here, is in the use of autonomous systems at the strategic level, to explore the remote terranes, potentially involving large surveys or detailed reconnaissance.

Clark, P. E.↗
NASA NTRS2012

Applying Biomimetic Algorithms for Extra-Terrestrial Habitat Generation

The objective is to simulate and optimize distributed cooperation among a network of robots tasked with cooperative excavation on an extra-terrestrial surface. Additionally to examine the concept of directed Emergence among a group of limited artificially intelligent agents. Emergence is the concept of achieving complex results from very simple rules or interactions. For example, in a termite mound each individual termite does not carry a blueprint of how to make their home in a global sense, but their interactions based strictly on local desires create a complex superstructure. Leveraging this Emergence concept applied to a simulation of cooperative agents (robots) will allow an examination of the success of non-directed group strategy achieving specific results. Specifically the simulation will be a testbed to evaluate population based robotic exploration and cooperative strategies while leveraging the evolutionary teamwork approach in the face of uncertainty about the environment and partial loss of sensors. Checking against a cost function and 'social' constraints will optimize cooperation when excavating a simulated tunnel. Agents will act locally with non-local results. The rules by which the simulated robots interact will be optimized to the simplest possible for the desired result, leveraging Emergence. Sensor malfunction and line of sight issues will be incorporated into the simulation. This approach falls under Swarm Robotics, a subset of robot control concerned with finding ways to control large groups of robots. Swarm Robotics often contains biologically inspired approaches, research comes from social insect observation but also data from among groups of herding, schooling, and flocking animals. Biomimetic algorithms applied to manned space exploration is the method under consideration for further study.

Birge, Brian↗