哈佛大学创造出像真实昆虫一样工作的机器人蚂蚁,用于建造和拆解复杂的结构。
Harvard Creates Robot Ants That Work Like Real Insects To Build And Dismantle Complex Structures

原始链接: https://www.zerohedge.com/technology/harvard-creates-robot-ants-work-real-insects-build-dismantle-their-own

## 机器人蚂蚁展示自组织建造 哈佛大学的研究人员开发了“RAnts”,这是一群受蚂蚁群体启发的简单机器人,能够在没有预编程或中央控制的情况下建造和拆除结构。这些机器人不使用复杂的AI,而是利用一种名为“具身智能”的原理,通过与环境的互动产生集体问题解决能力。 RAnts 使用“光信息素”——模拟蚂蚁信息素的光场——来协调诸如运输和放置积木等动作。通过遵循追踪光梯度和响应积木阈值等基本规则,蜂群会自发地组织起来,展示一种名为“刺激反应”的过程。 值得注意的是,蜂群可以通过调整两个参数即可瞬间在建造和拆除之间切换。这种去中心化的方法为自主机器人提供了一种鲁棒且适应性强的模型,潜在应用领域包括危险环境建造、行星探索,甚至动物行为研究。这项研究强调,智能并非仅仅关于强大的处理器,而是关于简单的主体与其周围环境有效互动。

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原文

Authored by Mrigakshi Dixit via Interesting Engineering,

Researchers at Harvard have developed a fleet of robotic ants that mimic the self-organizing behavior of social insects to build and dismantle structures without blueprints or central leadership.

An illustration of how the collective, decentralized behavior of ants has inspired experiments with cooperative robots that can complete tasks without central control.

Dubbed “RAnts”, these robotic ants have been designed by researchers from the John A. Paulson School of Engineering and Applied Sciences (SEAS). 

These are simple, decentralized robots that can spontaneously organize to build — and just as easily destroy — complex structures.

Instead of chemical pheromones, these robots use light fields (photormones) to communicate.

Our new study shows how simple, local rules can lead to the emergence of complex task completion that is self-organized and thus robust and adaptive,” said Professor L. Mahadevan, the Lola England de Valpine Professor of Applied Mathematics, Organismic and Evolutionary Biology, and Physics at SEAS and FAS.

“We also introduce the concept of exbodied intelligence, where collective cognition arises not solely from individual agents, but from their ongoing interaction with an evolving environment,” Mahadevan added.

Digital pheromones

Ants prove that you don’t need a big brain to be a great builder. All that is needed is a great team. Without blueprints or supervisors, these tiny creatures construct some of nature’s most complex habitats.

And now, experts are taking this cue. In recent years, AI development has obsessed over faster chips and bigger digital brains. 

But Professor L. Mahadevan and his team looked elsewhere, particularly exbodied intelligence.

In this model, the smart systems aren’t located inside the robot’s hardware. Rather, the intelligence emerges from the interaction between the robot and its surroundings. 

This study demonstrates that decentralized agents can achieve complex goals by following minimal physical rules and responding to environmental cues.

In the wild, ants communicate via pheromones — chemical breadcrumbs that signal where to walk or where to dig. To replicate this, the Harvard team used photormones.

Using a biological concept called stigmergy, in which individuals respond to environmental changes made by others, the team created “RAnts” that communicate through light fields known as photormones. 

These digital signals act as a substitute for natural pheromones, allowing the robots to coordinate their actions by sensing and modifying their surroundings in a continuous feedback loop.

Diverse use

Following simple gradients in a “photormone” light field, these robots create a feedback loop that coordinates the entire swarm. 

These operate on just a few basic rules, like tracking signals, transporting blocks, and depositing them at specific thresholds. 

The beauty of the system lies in its simplicity. Interestingly, the swarm can switch roles instantly by adjusting just two parameters: the intensity of the light-following behavior and the setting for dropping or picking up blocks.

One minute, the robots are a construction crew, and the next, a demolition team.

This development offers a new model for autonomous robotics, proving that sophisticated, large-scale tasks can be managed through simple, self-organizing interactions.

It suggests that collective intelligence isn’t just in the robots’ brains, but arises from the constant interaction between the agents and their evolving environment.

These findings pave the way for diverse applications, ranging from autonomous construction in hazardous zones and planetary exploration to the creation of advanced experimental models for analyzing animal behavior.

The study findings were detailed in the journal PRX Life.

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