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ONR: Robot Ecologies - Biologically Inspired Heterogeneous Teams

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  Overview

This project constitutes a systematic investigation of biologically inspired principles for prescribing and analyzing capabilities, roles, and control strategies for different individuals in teams of autonomous agents. In particular, the role of heterogeneity is studied from a biologically inspired vantage-point in order to enable teams to respond to environmental changes in robust, effective, safe, and predictable ways.

We propose to shift focus from the traditional coordination problem for networked, autonomous agents - focusing on how control and coordination strategies should be designed for a particular set of objectives and tasks - to the question of what these objectives and tasks should be? For example, we know how to do formation control, but what formations should we use in the first place? Similarly, we know how to assign roles to different agents and incorporate heterogeneity into teams. However, we are yet to study what heterogeneous roles are beneficial given a target application? Under the banners of bio-inspired and bio-mimetic multi-agent robotics, we propose to investigate if there is an argument to be made for having robots with different capabilities and characteristics working together, such as slow robots and fast robots, in real-world settings. We focus explicitly on this shift of vantage-point using biologically inspired principles for designing and evolving networks of autonomous agents. In particular, the following key, inter-connected research questions will be pursued:

Temporal Heterogeneity: Robots operating at different time scales will be studied. In particular, the SlowBots will be introduced as a way of making explicit the benefits of using slow robots in conjunction with fast ones. Tree sloths, lorises, and natural ecosystems will be used as inspiration for this work.

General Heterogeneity Measures: Ultimately, a general theory of heterogeneous teams must be developed -  driven by biology but drawing inspiration from other fields, such as economics and sociology. This will provide such a general framework in the setting on teams of autonomous agents.

Heterogeneous Multi-Robot Teams: A number of different aspects of heterogeneous multi-robot teams will be investigated in order to cover, characterize, and understand heterogeneity along different dimensions.

This project is a collaboration between three Principle Investigators: two at the Georgia Institute of Technology, and one at the University of Pennsylvania. Under Dr. Ronald Arkin, the Mobile Robot Lab’s focus is on the temporal heterogeneity component. Specifically, the methods, challenges, and benefits of incorporating intentionally slow robots (SlowBots) into a team. Two veins of research have been explored. Based on existing ethological literature on sloths and slow lorises, ethograms highlighting the various behaviors of sloths and slow lorises were drawn and behaviors relevant to the design of a robotic controller were identified. For each of the behaviors, different behavioral coordination mechanisms have been explored, namely, Action Selection vs. Behavioral Fusion or a combination thereof.

SlowBots, as slow and persistent agents in their environment, experience significant changes in their environment. While a quadrotor running for thirty minutes can safely ignore the daily solar cycle, it is a significant factor for a SlowBot operating for weeks in the field. To address this challenge for SlowBots, an artificial circadian system has been designed to allow for a SlowBot to model the dynamics of its environment, predict how it will continue to change in the future, and thus adapt its behavior to the changes.

 
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