This paper examines the structure and performance of three control strategies for a regenerative life support system constrained by mass balance equations. A novel agent-based control strategy derived from economic models of markets is compared to two standard control strategies, proportional feedback and optimal control. The control systems require different amounts of knowledge about the underlying system dynamics, utilize different amounts of information about the current state of the system, and differ in their ability to achieve system-wide performance goals. Simulations illustrate the dynamic behavior of the life support system after it is perturbed away from its equilibrium state or nominal operating point under the three different control strategies. The performance of these strategies is discussed in the context of system-wide performance goals such as efficiency and robustness.
A model-based, distributed architecture integrates diverse components in a system designed for lunar and planetary surface operations: spacesuit biosensors, cameras, GPS, and a robotic assistant. The system transmits data and assists communication between the extra-vehicular activity (EVA) astronauts, the crew in a local habitat, and a remote mission support team. Software processes ("agents"), implemented in a system called Brahms, run on multiple, mobile platforms, including the spacesuit backpacks, all-terrain vehicles, and robot. These "mobile agents" interpret and transform available data to help people and robotic systems coordinate their actions to make operations more safe and efficient. Different types of agents relate platforms to each other ("proxy agents"), devices to software ("comm agents"), and people to the system ("personal agents"). A state-of-the-art spoken dialogue interface enables people to communicate with their personal agents, supporting a speech-driven navigation and scheduling tool, field observation record, and rover command system.An important aspect of the engineering methodology involves first simulating the entire hardware and software system in Brahms, and then configuring the agents into a runtime system. Design of mobile agent functionality has been based on ethnographic observation of scientists working in Mars analog settings in the High Canadian Arctic on Devon Island and the southeast Utah desert (Clancey 2002a). The Mobile Agents system is developed iteratively in the context of use, with people doing authentic work. This paper provides a brief introduction to the architecture and emphasizes the method of empirical requirements analysis, through which observation, modeling, design, and testing are integrated in simulated EVA operations.
Project BackgroundThe Mobile Agents project anticipates exploration of Mars, in which a crew of six people live in a habitat for many months. One long-term objective is to automate the
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