Abstract-- Artificial Potential Field (APF) methods are widely used for reactive flocking control, but they often suffer from challenges such as deadlocks and local minima, especially in the presence of obstacles. Existing solutions to address these issues are typically passive, leading to slow and inefficient collective navigation. As a result, many APF approaches have only been validated in obstacle-free environments or simplified, pseudo-3D simulations. This paper presents GO-Flock, a hybrid flocking framework that integrates planning with reactive APF-based control. GO-Flock consists of an upstream Perception Module, which processes depth maps to extract waypoints and virtual agents for obstacle avoidance, and a downstream Collective Navigation Module, which applies a novel APF strategy to achieve effective flocking behavior in cluttered environments. We evaluate GO-Flock against passive APF-based approaches to demonstrate their respective merits, such as their flocking behavior and the ability to overcome local minima. Finally, we validate GO-Flock through obstacle-filled environment and also hardware-in-the-loop experiments where we successfully flocked a team of nine drones--six physical and three virtual-- in a forest environment. I. INTRODUCTION Flocking behavior, commonly observed in nature, involves the collective and coordinated movement of groups, such as flocks of birds or schools of fish.

Robot builds a robot's brain: AI generated drone command and control station hosted in the sky Abstract --Advances in artificial intelligence (AI) including large language models (LLMs) and hybrid reasoning models present an opportunity to reimagine how autonomous robots such as drones are designed, developed, and validated. Here, we demonstrate a fully AI-generated drone control system: with minimal human input, an artificial intelligence (AI) model authored all the code for a real-time, self-hosted drone command and control platform, which was deployed and demonstrated on a real drone in flight as well as a simulated virtual drone in the cloud. The system enables real-time mapping, flight telemetry, autonomous mission planning and execution, and safety protocols--all orchestrated through a web interface hosted directly on the drone itself. Not a single line of code was written by a human. We quantitatively benchmark system performance, code complexity, and development speed against prior, human-coded architectures, finding that AI-generated code can deliver functionally complete command-and-control stacks at orders-of-magnitude faster development cycles, though with identifiable current limitations related to specific model context window and reasoning depth. This work sets a precedent for the autonomous creation of robot control systems and, more broadly, suggests a new paradigm for robotics engineering--one in which future robots may be largely co-designed, developed, and verified by artificial intelligence. In this initial work, a robot built a robot's brain. INTRODUCTION In Arnold Schwarzenegger's Terminator, the robots become self-aware and take over the world. In this paper, we take a first step in that direction: A robot (AI code writing machine) creates, from scratch, with minimal human input, the brain of another robot, a drone. Man vs. machine Legend has it that, in the 1870s, a human rail layer (John Henry) tried to beat a steam engine rail laying machine (robot) (Figure 1A). He died trying to beat the machine (robot). John Henry is an American legend and icon, similar to Johny Appleseed, Paul Bunyan, and George Washington. The United States Postal Service issued a postage stamp of him in 1996. According to a folk song from 1918, later popularized by Disney, and still sung by American schoolchildren to this day, the American labor legend'John Henry was a mighty man, born with a hammer right in his hand' ( 1). Peter J. Burke is with the Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92697 USA (e-mail: pburke@uci.edu). In this work, we demonstrate a similar result in robot control software.

A man with paralysis who had electrodes implanted in his brain can pilot a virtual drone through an obstacle course simply by imagining moving his fingers. His brain signals are interpreted by an AI model and then used to control a simulated drone. Brain-computer interface (BCI) research has made huge strides in recent years, allowing people with paralysis to precisely control a mouse cursor and dictate speech to computers by imagining writing words with a pen. But so far, they haven't yet shown great promise in complex applications with multiple inputs. Now, Matthew Willsey at the University of Michigan and his colleagues have created an algorithm that allows a user to trigger four discrete signals by imagining moving their fingers and thumb.

Last November, at Fort Campbell, Tennessee, half a mile from the Kentucky border, a single human directed a swarm of 130 robots. The exercise was part of DARPA's OFFensive Swarm-Enabled Tactics (OFFSET) program. If the experiment can be replicated outside the controlled settings of a test environment, it suggests that managing swarms in war could be as easy as point and click for operators in the field. "The operator of our swarm really was interacting with things as a collective, not as individuals," says Shane Clark, of Raytheon BBN, who was the company's main lead for OFFSET. "We had done the work to establish the sort of baseline levels of autonomy to really support those many-to-one interactions in a natural way."