Here Come the Robot Swarms! Machines, acting collectively, can accomplish tasks that are difficult for individual robots

  

Is there an open source AI, available from the shelf that could allow a swarm of small light drones move along a massive mother-drone, and be able to warn mother-drone to change direction to avoid crashing into things unexpectedly? That would help to explore places of limited visibility, like moving in fog at high speed. The fog in California is sometimes thick as milk.

 

There isn’t yet a single, complete open-source AI system that is "off-the-shelf" and fulfills all mentioned requirements for a mother-drone with a warning swarm.

 

The source: https://markaicode.com/autonomous-drone-swarms-python-ros2-guide/ provides the most directly relevant information with practical Python and ROS2 code examples for drone swarm coordination. For Drone Control & Communication open source MAVSDK-Python & PX4 Autopilot stack could be used. Design where follower drones publish obstacle data to a shared ROS topic; mother-drone subscribes and fuses this data for a comprehensive view is in the source above. Open source simple, effective logic for drones to maintain safe distances from obstacles and each other; suitable for real-time use on small drones is here: https://www.mdpi.com/1424-8220/25/4/1141

 

A Proposed System Architecture

 

You can integrate these tools to create a proof-of-concept system. The general workflow would be:

 

    Establish the Swarm: Use a ROS 2-based framework to create a network where each follower drone and the mother-drone operates as an independent node.

 

Share Sensor Data: Program the follower drones to act as mobile sensors. Each drone should publish its own position and, crucially, any detected obstacle data to a common ROS 2 topic (e.g., /swarm/obstacle_scan).

 

Fuse Data on the Mother-Drone: The mother-drone runs a node that subscribes to the obstacle topic. It aggregates this data from all followers to build a rich, real-time map of its surroundings, effectively "seeing" through the swarm.

 

Make Decisions: Implement a decision-making module on the mother-drone. Using the fused sensor data, it can calculate a new, safe trajectory using an avoidance algorithm.

 

Execute the Maneuver: The mother-drone changes its path and, because the follower swarm is coordinated (e.g., using leader-follower logic), the entire formation adjusts accordingly. More general description of robotic swarms follows:

 

“Forget teaching robots to think like humans. A field called swarm robotics is taking inspiration from ants, bees and even slime molds -- simple creatures that achieve remarkable feats through collective intelligence.

 

Unlike traditional robots that take orders from a central computer, swarm robots work like ant colonies. No single robot is in charge, but the swarm accomplishes complex tasks through simple interactions between neighbors. Each robot interacts only with those nearby, sometimes communicating with sounds or chemical signals in particles they release.

 

Researchers say this approach could excel where traditional robots fail, like situations where central control is impractical or impossible due to distance, scale or communication barriers.

 

For instance, a swarm of drones might one day monitor vast areas to detect early-stage wildfires that current monitoring systems sometimes miss. Hundreds of drones could be continuously patrolling, able to detect fires within minutes of ignition. If some drones fail, others would continue monitoring.

 

A human operator might set parameters like where to search, but the drones would independently share information like which areas have been searched, adjust search patterns based on wind and other weather data from other drones, and converge for more complete coverage of an area when one detects smoke.

 

In another potential application, a swarm of robots could make deliveries across wide areas more efficient by alerting each other to changing traffic conditions or redistributing packages among themselves if one breaks down.

 

Robot swarms could also manage agricultural operations in places without reliable internet service. And disaster-response teams see potential for swarms in hurricane zones where communication infrastructure has been destroyed.

 

At the microscopic scale, researchers are developing tiny robots that could work together to navigate the human body to deliver medication or clear blockages without surgery.

 

Part of what has kept swarm robotics in research instead of real-world applications has been cost. But the economics of the field have shifted in recent years, according to Sabine Hauert, a professor of swarm engineering at the University of Bristol in England.

 

Cheaper sensors, batteries and processors have made it possible for a much broader range of researchers to build and test robot swarms.

 

In recent demonstrations, teams of tiny magnetic robots -- each about the size of a grain of sand -- cleared blockages in artificial blood vessels by forming chains to push through the obstructions.

 

The robots navigate individually through blood vessels to reach a clog, guided by doctors or technicians using magnetic fields to steer them, says researcher J.J. Wie, a professor of organic and nano engineering at Hanyang University in South Korea. When they reach an obstruction, the robots coordinate with each other to team up and break through.

 

Wie's group is developing versions of these robots that biodegrade after use, eliminating the need for surgical removal, and coatings that make the robots compatible with human tissue. And while robots the size of sand grains work for some applications, Wie says that they will need to be shrunk to nano scale to cross biological barriers, such as cell membranes, or bind to specific molecular targets, like surface proteins or receptors on cancer cells.

 

Other researchers are exploring what happens when swarms move beyond any human-designed coordination. The phenomenon is known as emergent intelligence -- when simple machines, following only a few local cues, begin to organize and act as if they share a mind.

 

In one experiment, a group of robots were programmed with just three abilities -- move forward, emit sound and listen to neighbors -- then placed in a space with various obstacles and given no further instruction. They spontaneously linked together to form chains that slithered through some obstacles and surrounded other objects.

 

"Each robot has minimal functionality," says Igor Aronson, an engineer at Pennsylvania State University who led the project. "But together, they show behaviors that look intelligent."

 

The research draws inspiration from single-celled slime molds that self-organize when starving. While the molds use chemical signals to coordinate, Aronson and his collaborators chose acoustic communication instead, since sound waves travel faster than chemical signals, making the approach more practical for robotic applications.

 

The work suggests that sophisticated coordination might not require sophisticated machines. Intelligence, in this case, isn't coded in advance but emerges from the way simple parts interact. And as those parts grow slightly more capable -- say, with improved sensors or processing power -- the behavior could evolve too.

 

"If you add a little bit more complexity," Aronson says, "you would expect even more intelligence to emerge."

 

---

 

Jackie Snow is a writer in Los Angeles. She can be reached at reports@wsj.com.” [1]

 

1. Artificial Intelligence (A Special Report) --- Here Come the Robot Swarms! Machines, acting collectively, can accomplish tasks that are difficult for individual robots. Snow, Jackie.  Wall Street Journal, Eastern edition; New York, N.Y.. 03 Nov 2025: R6.