Multi-agent systems collaborate to overcome sensing limits in complex environments
General Collaborative Intelligence: Architecting Cognition for Resilient Multi-Agent Ecosystems
Computer Vision and Pattern RecognitionArtificial IntelligenceRobotics
Summary
Many unmanned systems like drones or self-driving cars struggle to see everything around them due to blocks, bad weather, or limited sensors. The authors review how groups of these systems can work together by sharing key information to understand the world better than alone. They organize this teamwork in five main areas and introduce three ideas to make these collaborations smarter. The review also discusses challenges like making systems reliable and fair, and proposes a way to fairly compare different collaboration methods. This helps guide future work towards smarter and more resilient multi-agent systems.
What this means in practice
- •For autonomous vehicle teams: Improve coordinated perception and decision-making among vehicles to better handle occlusions and sensor limits on the road.
- •For industrial logistics operators: Enhance collaboration protocols for multiple robotic agents to navigate and operate efficiently in complex warehouse environments.
A survey. It maps existing work.
Authors
Lei Zhang, Chun Ye, Le Yang, Zhaozhong Wang, Deng-Ping Fan, Hang Dai, Binglu Wang
Abstract
Multi-agent unmanned systems are moving from isolated, ego-centric sensing toward collaborative intelligence, in which distributed agents exchange compact features to overcome a local observation trap that no single agent can escape: occlusions, finite sensor range, and environmental degradation. The field has matured across architectural, communication, embodied, resilience, and trust dimensions, yet existing surveys examine these dimensions in isolation and rarely expose their dependencies. This review offers a unified synthesis through two complementary lenses. The first is a five-dimensional taxonomy spanning collaboration stage, communication paradigm, fusion architecture, learning strategy, and application domain. The second is three cognitive synergy conditions, Semantic Disambiguation, Pragmatic Information Exchange, and Proactive Informational Foraging, that turn cognitive synergy into operational criteria. Across these lenses we survey collaboration architectures and topologies, neural-communication co-design that treats the channel as a differentiable pipeline component, embodied action-perception loops via multi-agent reinforcement learning, and resilience mechanisms for synchronization, uncertainty quantification, and label-efficient learning. We then map these advances onto four operational domains, V2X, unmanned aerial, industrial logistics, and smart cities, and onto the safety-privacy-utility triad. To counter benchmark saturation and evaluation fragmentation, we propose GCI-Bench, a five-pillar scoring protocol with a maturity model that makes the trade-offs of collaborative methods comparable across studies. A critical reflection on reproducibility, the sim-to-real gulf, and conditions under which collaboration degrades performance identifies open challenges and charts directions toward general collaborative intelligence under real-world uncertainty.