Swarm Coordination: Aligning Robots Without Knowing Their Speed

Author: Denis Avetisyan

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Researchers have developed a new position-based flocking model that enables robotic swarms to maintain stable formations and collective alignment without relying on direct velocity measurements.

This work introduces a method for persistent alignment in multi-robot systems by approximating velocity from relative positions and implementing a gain-based alignment strategy.

Maintaining coherent collective motion in multi-agent systems is often challenged by reliance on accurate velocity sensing, which can be unreliable in real-world deployments. This paper introduces a novel approach, ‘Position-Based Flocking for Persistent Alignment without Velocity Sensing’, that achieves stable flocking behavior by approximating velocity from relative position changes. By incorporating a time- and density-dependent alignment gain with a non-zero minimum threshold, the model sustains directional alignment and compact formations without direct velocity measurements-demonstrated through both simulation and experiments with a team of nine robots. Could this position-based method unlock more robust and scalable swarm robotics applications in dynamic and uncertain environments?

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Decoding the Swarm: Why Constant Monitoring is the Enemy of Collective Motion

Many established strategies for achieving collective motion in groups – often inspired by bird flocks or fish schools – fundamentally depend on each individual accurately perceiving the position and velocity of its neighbors. These velocity alignment models require constant updates on each agent’s state to maintain cohesion and coordinated movement. However, this continuous sensing presents a significant challenge when applied to engineered systems like robotic swarms, as real-world sensors are imperfect and prone to noise. Furthermore, maintaining constant communication channels to share this state information consumes valuable bandwidth and processing power, potentially limiting the scalability and responsiveness of the swarm. Consequently, the reliance on precise, continuous state estimation becomes a bottleneck for deploying robust collective behaviors in practical, resource-constrained environments.

Real-world robotic swarms face significant hurdles when attempting to replicate the elegant coordination seen in natural flocks. The precision demanded by traditional flocking algorithms – continuous monitoring of each neighbor’s position and velocity – proves problematic due to inherent limitations in physical systems. Sensor noise introduces inaccuracies in perceived states, while the bandwidth available for communication between robots is often restricted, preventing the rapid and reliable exchange of information. Furthermore, the computational power available on individual robots is finite, meaning complex calculations required for precise state estimation and control become increasingly difficult as the swarm grows. These constraints collectively necessitate the development of coordination strategies that are resilient to imperfect information and minimize the computational burden on each robot, allowing swarms to function effectively in noisy, bandwidth-limited, and computationally constrained environments.

The pursuit of truly scalable swarm robotics hinges on moving beyond coordination methods that demand perfect knowledge of each individual’s state. Current flocking algorithms frequently assume robots can accurately perceive the position and velocity of their neighbors, a proposition quickly undermined by the realities of sensor limitations and communication bottlenecks. Consequently, research is increasingly focused on strategies that prioritize relative information – such as local interactions and minimal sensing – to achieve collective behaviors. These approaches aim to create robustness against noise and uncertainty, allowing swarms to maintain cohesion and accomplish tasks even when precise state estimation is impractical or impossible. By minimizing reliance on absolute knowledge, these efficient coordination strategies pave the way for deploying larger, more resilient robotic swarms in complex and dynamic environments.

Stripping Away the Complexity: Position-Based Flocking as a System Reset

Position-Based Flocking represents a departure from traditional flocking algorithms which typically utilize velocity-based interactions. Instead of agents reacting to the velocities of their neighbors, this strategy focuses on relative positional data. Each agent calculates its movement based on its position relative to nearby agents, effectively creating a coordination mechanism that does not require direct velocity measurements. This positional interaction is calculated using the positions of all agents within a defined radius, and the resulting force is applied to influence the agent’s trajectory, leading to collective motion. The algorithm defines a target distance between agents, and movement is adjusted to minimize deviations from this target, facilitating cohesive flock behavior.

Position-Based Flocking reduces system complexity by eliminating the necessity for direct velocity measurements. Traditional flocking algorithms require each agent to accurately determine the velocities of nearby agents, demanding sophisticated sensor hardware or computationally expensive tracking methods. This approach instead relies solely on relative positional data, which can be obtained through simpler, less precise, and therefore less resource-intensive sensors. Consequently, the computational burden associated with velocity estimation and tracking is removed, resulting in reduced processing requirements and enabling implementation on systems with limited computational capacity or power budgets.

Position-based flocking achieves coordinated movement by each agent calculating its steering direction based on the relative positions of nearby agents, rather than requiring direct measurement of their velocities. Each agent determines its desired movement to align with the average position of its neighbors within a defined radius; this calculation inherently promotes cohesion and avoids collisions without explicitly knowing how fast other agents are moving. The system computes a vector pointing towards the perceived flock center, and the agent adjusts its trajectory to move towards this point, effectively maintaining group coherence through positional relationships alone. This approach minimizes the reliance on accurate velocity data, simplifying the sensing and processing requirements for each agent and increasing the robustness of the flocking behavior.

To ensure flock integrity and stability, the Position-Based Flocking implementation incorporates a ‘weak memory’ of each agent’s initial position. This is not a persistent, exact record, but rather a decaying average of the starting location, updated with each time step. The influence of this initial position diminishes over time, preventing agents from being rigidly tethered to their starting points while still providing a foundational reference for maintaining overall flock cohesion. This approach allows for dynamic flocking behavior, accommodating agent movement and adjustments without requiring continuous recalculation of absolute positions, and prevents the flock from dispersing due to accumulated errors.

Proof of Concept: Validating Position-Based Flocking in the Physical World

Simulations assessing Position-Based Flocking (PBF) consistently showed performance levels equal to or exceeding those of established flocking algorithms when evaluating cohesion and alignment. Specifically, PBF demonstrated comparable or improved metrics related to maintaining inter-agent proximity and minimizing divergence from the overall flock heading. These results were obtained through repeated trials varying flock size and environmental complexity, indicating the robustness of PBF across different operational scenarios. The simulation framework allowed for controlled comparisons, isolating the impact of PBF’s positional focus against velocity-based methods, and consistently demonstrated its ability to achieve stable flocking behavior with similar or reduced computational cost.

Real-world validation of the Position-Based Flocking algorithm was performed utilizing the GRITSBot robotic platform in a dynamic and noisy environment. These experiments were designed to assess the algorithm’s robustness beyond simulation, specifically evaluating its performance under conditions representative of practical robotic deployments. The GRITSBot platform facilitated testing with multiple agents operating concurrently, allowing for the evaluation of scalability characteristics. Data collected from these physical trials confirmed the algorithm’s ability to maintain flock cohesion and alignment despite sensor inaccuracies and environmental disturbances, demonstrating its potential for real-world application in multi-robot coordination tasks.

Accurate relative position measurements were critical to evaluating the Position-Based Flocking algorithm, and were obtained through a multi-sensor approach. Ultra-Wideband (UWB) radio technology provided range estimates between robots, while LiDAR data supplemented this with local obstacle and inter-agent distance information. For ground truth validation and to address potential UWB and LiDAR inaccuracies, a Vicon motion capture system was employed. This system utilized infrared cameras to track retroreflective markers affixed to each robot, providing highly precise positional data used for performance metric calculation and algorithm validation.

Experimental results demonstrate that Position-Based Flocking effectively coordinates multi-robot systems without requiring precise velocity measurements. The approach consistently achieved a sustained alignment metric of approximately ≈1, indicating strong directional consistency among agents. This performance was observed across experiments utilizing the GRITSBot platform, confirming the method’s ability to maintain flock cohesion solely through relative positioning data. The ability to function without accurate velocity readings reduces sensor requirements and computational load, enhancing the scalability and robustness of the flocking algorithm in real-world deployments.

Experimental results indicate that Position-Based Flocking facilitates the maintenance of tighter formations compared to velocity-alignment methods. Simulations consistently demonstrated stable inter-agent separations ranging from 1.5 to 2 meters. Physical experiments, conducted with the GRITSBot platform, confirmed a 0.75-meter interaction radius amongst agents. This reduced separation distance suggests an increased density of the flock and improved coordination capabilities without relying on precise velocity data, effectively enabling more compact and efficient collective movement.

Beyond Mimicry: How Position-Based Flocking Reshapes Bio-Inspired Robotics

Position-based flocking demonstrates a powerful translation of biological coordination strategies to the realm of robotics. Observing natural swarms – from bird flocks to fish schools – reveals that individuals often maintain cohesion not through constant communication about absolute positions, but by responding to the relative positions of their immediate neighbors. This principle is replicated in robotic swarms by programming each robot to adjust its movement based on the perceived distance and bearing to nearby units, rather than relying on a central controller or complex global maps. The result is a surprisingly robust and scalable system; even if individual robots experience sensor failures or communication disruptions, the swarm can maintain its cohesive structure and continue functioning effectively, mirroring the resilience observed in natural flocks and offering a promising avenue for developing adaptable and decentralized robotic systems.

A significant advantage of Position-Based Flocking lies in its diminished need for sophisticated sensors and constant communication between robots. Traditional multi-robot coordination often demands precise distance measurements and frequent data exchange, creating bottlenecks and vulnerabilities as swarm size increases. This approach, however, prioritizes relative positioning – each robot reacting to the positions of its immediate neighbors – drastically reducing the computational burden and communication overhead. Consequently, the system becomes more robust to sensor noise, communication failures, and individual robot malfunctions, as localized interactions can compensate for imperfect information. Moreover, the simplicity of the rules governing robot behavior facilitates effortless scalability; adding more robots to the swarm doesn’t exponentially increase the complexity of the coordination process, paving the way for truly large-scale robotic deployments.

The elegance of Position-Based Flocking isn’t limited to achieving coherent group movement; its core principles demonstrate broad applicability to various bio-inspired robotic tasks. By focusing on relative positioning rather than absolute coordinates or complex communication, this approach offers a robust framework for coordinating robots engaged in activities like collective foraging, where maintaining spatial relationships is crucial for efficient resource discovery. Similarly, the technique holds promise for collective manipulation – enabling a swarm of robots to cooperatively lift, move, or assemble objects – as it simplifies coordination by reducing the need for precise individual control and instead emphasizing local interactions. This adaptability suggests that Position-Based Flocking may serve as a foundational strategy for designing more versatile and resilient multi-robot systems capable of tackling a wider range of complex challenges inspired by natural collective behaviors.

Investigations into Position-Based Flocking are poised to expand beyond simplified simulations and address the challenges of real-world application. Future studies will likely focus on adapting the core principles to accommodate dynamic environments – those featuring moving obstacles or changing goals – and more intricate interaction scenarios, such as coordinated navigation through cluttered spaces. This adaptation will necessitate leveraging a deeper understanding of Collective Motion, potentially incorporating elements of predictive behavior and responsive collision avoidance. Such research aims to move beyond basic flocking behaviors toward robust, adaptable robotic swarms capable of complex tasks, mirroring the sophisticated coordination observed in natural biological systems and paving the way for applications in search and rescue, environmental monitoring, and collaborative construction.

The pursuit of stable, collective motion, as demonstrated in this position-based flocking model, echoes a fundamental principle of system understanding: probing boundaries to reveal underlying mechanisms. It’s a process of intellectual disassembly, akin to reverse-engineering a complex organism to discern its operational logic. Grace Hopper famously stated, “It’s easier to ask forgiveness than it is to get permission.” This sentiment encapsulates the exploratory spirit driving this research – a willingness to deviate from traditional velocity-based approaches and forge a new path based on positional relationships, even if it requires challenging established norms in swarm robotics to achieve persistent alignment and robust formations.

Pushing the Boundaries of the Flock

The presented work successfully demonstrates alignment without direct velocity sensing, a clever sidestep of a traditionally fundamental requirement. However, to truly interrogate this system, one must ask: what happens when the assumptions break down? This model relies on relative positioning to infer velocity. Introducing asynchronicity – a robot occasionally ‘missing’ a positional update, or experiencing significant sensor noise – immediately stresses this inference. Does the flock fracture, or self-correct? Exploring the limits of this positional approximation is not merely a robustness test, but a path to understanding how living flocks manage imperfect information.

Furthermore, the current formulation prioritizes alignment and stability. But natural flocks are rarely static. What if the goal isn’t just to maintain formation, but to dynamically reconfigure it – to split, merge, or navigate complex obstacles? This necessitates an investigation into how this position-based control can be extended to accommodate more nuanced behavioral goals. A truly robust system shouldn’t just avoid falling apart; it should gracefully adapt to unforeseen circumstances and shifting priorities.

Ultimately, the most intriguing question is this: how much can one truly abstract away from the underlying physics? This work is a compelling step towards minimizing the required sensory input, but it also begs the question of whether there exists a minimal viable ‘flocking’ system – a set of rules so simple, so elegant, that it transcends the need for detailed velocity estimation, and instead relies purely on spatial relationships. To find that limit – to deliberately break the rules until only the essential components remain – is where the real innovation lies.

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Original article: https://arxiv.org/pdf/2602.22154.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-02-27 05:59