Autonomous Agents and Intelligent Assistants for Exploration Operations

Human exploration of space will involve remote autonomous crew and systems in long missions. Data to earth will be delayed and limited. Earth control centers will not receive continuous real-time telemetry data, and there will be communication round trips of up to one hour. There will be reduced human monitoring on the planet and earth. When crews are present on the planet, they will be occupied with other activities, and system management will be a low priority task. Earth control centers will use multi-tasking "night shift" and on-call specialists. A new project at Johnson Space Center is developing software to support teamwork between distributed human and software agents in future interplanetary work environments. The Engineering and Mission Operations Directorates at Johnson Space Center (JSC) are combining laboratories and expertise to carry out this project, by establishing a testbed for hWl1an centered design, development and evaluation of intelligent autonomous and assistant systems. Intelligent autonomous systems for managing systems on planetary bases will commuicate their knowledge to support distributed multi-agent mixed-initiative operations. Intelligent assistant agents will respond to events by developing briefings and responses according to instructions from human agents on earth and in space

A middleware for a large array of cameras

Large arrays of cameras are increasingly being employed for producing high quality image sequences needed for motion analysis research. This leads to the logistical problem with coordination and control of a large number of cameras. In this paper, we used a lightweight multi-agent system for coordinating such camera arrays. The agent framework provides more than a remote sensor access API. It allows reconfigurable and transparent access to cameras, as well as software agents capable of intelligent processing. Furthermore, it eases maintenance by encouraging code reuse. Additionally, our agent system includes an automatic discovery mechanism at startup, and multiple language bindings. Performance tests showed the lightweight nature of the framework while validating its correctness and scalability. Two different camera agents were implemented to provide access to a large array of distributed cameras. Correct operation of these camera agents was confirmed via several image processing agents

Remote Access to a Prototyping Laboratory

There is a growing global demand for continuing adult higher education particularly in science and engineering subjects. New technologies are emerging which would enable the development of a Remote Access Laboratory for rapid prototyping of Artificial Intelligence, as a learning environment for mechatronic engineering, in which high precision electromechanical devices are designed to exhibit autonomous behaviour. Secondary research investigated the learning theories for a Remote Access Laboratory, and the current practices for distance learning, involving groupware in shared activity 'collaboratories'. Having determined that the laboratory would need a multi-user interactive environment architecture, with the requirement for adaptability to rapid developments,a distributed software architecture was selected. The laboratory design was subsequently argued to be best served by Intelligent Agents in a Multi-Agent system. The aims of the research were to establish the viability of a Remote Access Laboratory for mechatronic experimentation, and to evaluate the technologies required to implement such a laboratory environment for rapid prototyping. These were achieved by developing a novel user interface, based on a multi-functional screen layout, and a graphical specification facility to provide robotic navigation that is intuitive to use and does not require text-based programming. The research investigated the prototyping of robotic behaviour, which used Programming by Demonstration as an innovative technique to prototype robot navigation. The method of designing behaviours met an anticipated need to allow the robot to interact with an environment, to achieve goals under conditions of uncertainty, while requiring a level of abstraction in the behaviour design. The interface structured a composite of the designed behaviours into prototype Artificial Intelligence using a hierarchical behaviour architecture, which complied with the principles of Object Orientated programming. This was subsequently a new and original programming method to facilitate rapid prototyping of Artificial Intelligence design and structuring. Experimentation involved 20 participants attempting to accomplish a series of tasks which involved using the prototyped interface and an existing text-based robot programming system. The participants were profiled by their formal qualifications, knowledge and experience. The experimental data obtained were used to establish a comparative measure of the prototype interface success compared with an existing distance-learning, home experiment kit, in the form of a small controllable model vehicle. The data obtained provided strong evidence to support the hypothesis that a Programming by Demonstration based system for rapid prototyping is more flexible and easier to use than a previously existing distance learning text-based system. The Programming by Demonstration system showed great promise, being quicker for prototyping, and more intuitive. The learning interface design pioneered new techniques and technologies for rapid prototyping of Artificial Intelligence in a Mechatronics Remote Access Laboratory

A middleware for a large array of cameras

Large arrays of cameras are increasingly being employed for producing high quality image sequences needed for motion analysis research. This leads to the logistical problem with coordination and control of a large number of cameras. In this paper, we used a lightweight multi-agent system for coordinating such camera arrays. The agent framework provides more than a remote sensor access API. It allows reconfigurable and transparent access to cameras, as well as software agents capable of intelligent processing. Furthermore, it eases maintenance by encouraging code reuse. Additionally, our agent system includes an automatic discovery mechanism at startup, and multiple language bindings. Performance tests showed the lightweight nature of the framework while validating its correctness and scalability. Two different camera agents were implemented to provide access to a large array of distributed cameras. Correct operation of these camera agents was confirmed via several image processing agents

Safe, Remote-Access Swarm Robotics Research on the Robotarium

This paper describes the development of the Robotarium -- a remotely accessible, multi-robot research facility. The impetus behind the Robotarium is that multi-robot testbeds constitute an integral and essential part of the multi-agent research cycle, yet they are expensive, complex, and time-consuming to develop, operate, and maintain. These resource constraints, in turn, limit access for large groups of researchers and students, which is what the Robotarium is remedying by providing users with remote access to a state-of-the-art multi-robot test facility. This paper details the design and operation of the Robotarium as well as connects these to the particular considerations one must take when making complex hardware remotely accessible. In particular, safety must be built in already at the design phase without overly constraining which coordinated control programs the users can upload and execute, which calls for minimally invasive safety routines with provable performance guarantees.Comment: 13 pages, 7 figures, 3 code samples, 72 reference