Swarm sign language: motion-based communication between drones

• URL: http://arxiv.org/abs/2606.27883v1
• Date: Fri, 26 Jun 2026 09:25:22 GMT
• Title: Swarm sign language: motion-based communication between drones
• Abstract summary: In swarm-constrained robotics, visual communication provides a critical alternative to active radio transmissions, which might be jammed.<n>This research investigates motion-based communication for non-active information exchange, utilizing modular, dynamically feasible planar trajectories as visual cues.
• Score: 42.23500604789643
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In stealth-constrained swarm robotics, visual communication provides a critical alternative to active radio transmissions, which might be jammed. This research investigates motion-based communication for non-active information exchange, utilizing modular, dynamically feasible planar trajectories as visual cues. On the receiver drone end, a pose estimator tracks the transmitting drone's pose, feeding it into our custom 3DTrajDecoder. The decoder is designed to classify and segment the spatiotemporal sequence while simultaneously regressing its size and normal vector. To robustly train the decoder on both communicative and non-communicative trajectories, we developed a configurable online procedural generation pipeline. We validate our system through real-world testing and simulation to define its operating domain, supported by an extensive ablation study detailing our architectural choices and system limitations.

Related papers

• Remotely Detectable Keyed Communication through Motion [10.256545668234795]
  We introduce a method for messaging through motion capable of encoding arbitrary message content over short payloads.<n>This brings a new kind of robustness to robot communication.<n>Four robots jointly recover an 8-bit message at an aggregate 0.67 bits/s.
  arXiv  Detail & Related papers  (2026-09-08T15:49:10Z)
• MoCom: Motion-based Inter-MAV Visual Communication Using Event Vision and Spiking Neural Networks [5.637448828599577]
  We propose a novel visual communication framework for MAV swarms using motion-based signaling.<n>In this framework, MAVs convey information, such as heading and distance, through deliberate flight patterns.<n>Results validate the framework's effectiveness, which demonstrates accurate decoding and low power consumption.
  arXiv  Detail & Related papers  (2025-10-16T15:06:51Z)
• Agent-driven Generative Semantic Communication with Cross-Modality and Prediction [57.335922373309074]
  We propose a novel agent-driven generative semantic communication framework based on reinforcement learning. In this work, we develop an agent-assisted semantic encoder with cross-modality capability, which can track the semantic changes, channel condition, to perform adaptive semantic extraction and sampling. The effectiveness of the designed models has been verified using the UA-DETRAC dataset, demonstrating the performance gains of the overall A-GSC framework.
  arXiv  Detail & Related papers  (2024-04-10T13:24:27Z)
• Joint Sensing and Task-Oriented Communications with Image and Wireless Data Modalities for Dynamic Spectrum Access [49.83882366499547]
  This paper introduces a deep learning approach to dynamic spectrum access, leveraging the synergy of multi-modal image and spectrum data for the identification of potential transmitters. We consider an edge device equipped with a camera that is taking images of potential objects such as vehicles that may harbor transmitters. We propose a collaborative system wherein the edge device communicates selectively processed information to a trusted receiver acting as a fusion center.
  arXiv  Detail & Related papers  (2023-12-21T15:26:26Z)
• Dynamic Event-based Optical Identification and Communication [1.7289819674602298]
  temporal pattern recognition, depending on the technology, involves a trade-off between communication frequency, range and accurate tracking. We propose a solution with light-emitting beacons that improves this trade-off by exploiting fast event-based cameras. We demonstrate for the first time beacon tracking performed simultaneously with state-of-the-art frequency communication in the kHz range.
  arXiv  Detail & Related papers  (2023-03-13T15:12:30Z)
• Is Semantic Communications Secure? A Tale of Multi-Domain Adversarial Attacks [70.51799606279883]
  We introduce test-time adversarial attacks on deep neural networks (DNNs) for semantic communications. We show that it is possible to change the semantics of the transferred information even when the reconstruction loss remains low.
  arXiv  Detail & Related papers  (2022-12-20T17:13:22Z)
• Unified Control Framework for Real-Time Interception and Obstacle Avoidance of Fast-Moving Objects with Diffusion Variational Autoencoder [2.5642257132861923]
  Real-time interception of fast-moving objects by robotic arms in dynamic environments poses a formidable challenge. This paper introduces a unified control framework to address the challenge by simultaneously intercepting dynamic objects and avoiding moving obstacles.
  arXiv  Detail & Related papers  (2022-09-27T18:46:52Z)
• Decoder Fusion RNN: Context and Interaction Aware Decoders for Trajectory Prediction [53.473846742702854]
  We propose a recurrent, attention-based approach for motion forecasting. Decoder Fusion RNN (DF-RNN) is composed of a recurrent behavior encoder, an inter-agent multi-headed attention module, and a context-aware decoder. We demonstrate the efficacy of our method by testing it on the Argoverse motion forecasting dataset and show its state-of-the-art performance on the public benchmark.
  arXiv  Detail & Related papers  (2021-08-12T15:53:37Z)
• Spatial-Channel Transformer Network for Trajectory Prediction on the Traffic Scenes [2.7955111755177695]
  We present a Spatial-Channel Transformer Network for trajectory prediction with attention functions. A channel-wise module is inserted to measure the social interaction between agents. We find that the network achieves promising results on real-world trajectory prediction datasets on the traffic scenes.
  arXiv  Detail & Related papers  (2021-01-27T15:03:42Z)
• Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance [95.86944752753564]
  We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
  arXiv  Detail & Related papers  (2020-07-28T07:34:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.