NSF-CONACyT: Ecological Robotics: A Schema-Theoretic Approach

根据美国-墨西哥CONACyT倡议，该奖项旨在深入科学地理解移动机器人代理与其环境之间的关系的性质。本研究利用图式理论和神经生理学和行为学模型，直接研究了作用力与环境的关系，最终在这一领域提供了可信的、可推广的和有用的结果。这些系统最终将以模拟和实际的机器人硬件为基础。对机器人与其环境(即整体生态)的关系进行深入的理解和动态建模是重要的，以确保现场的机器人系统不会与能够更有效地完成任务的其他代理进行不适当的竞争(通过竞争，降低其无用性)；确保机器人在生态系统中是成功的竞争对手，并可能取代效率较低的代理；它们具有生态敏感性，以便无论何时引入新的机器人技术，代理-环境系统的动态都是良好的建模和尽可能可预测的。研究工作将通过使用两个地点已有或正在开发的软件工具联系在一起，包括神经模拟语言NSL、抽象模式语言ASL和以模式为基础的面向任务的模拟和机器人实施环境MissLab。

This award, under the U.S.-Mexico CONACyT Initiative, is concerned with an in-depth scientific understanding of the nature of the relationship between mobile robotic agents and their environment. The research addresses the agent-environment relationship directly using schema theory and neurophysiological and ethological models to ultimately provide credible, generalizable, and useful results in this domain. These systems will ultimately be grounded in both simulations and actual robotic hardware. An in-depth understanding and dynamic modeling of the relationship a robot has with its environment (i.e., the overall ecology) is important to ensure that fielded robotic systems don't compete inappropriately with other agents that can do the task more effectively (and by competing, reduce their usefuless); that the robots are successful competitors within the ecological system and can potentially displace less efficient agents; and that they are ecologically sensitive so that agent-environmental system dynamics are well-modeled and as predictable as possible whenever new robotic technology is introduced. The research efforts will be tied together through the use of software tools already in existence or under development at both sites, including NSL, a neural simulation language; ASL, an abstract schema language; and MissionLab, a schema-based mission-oriented simulation and robot implementation environment.