Distributed Autonomous Swarm Navigation in GPS-Denied Environments

Abstract

This work presents a method for maintaining collective positional confidence in a distributed autonomous swarm operating in environments where GPS and external communication infrastructure are unavailable or actively denied. Terrain data for the defined area of operations is preloaded onto each node prior to deployment, providing the inference basis for localization without reliance on live external signals. Each node performs rapid observation of environmental anomalies and treats successive observations as states in a Markov chain. Confidence in positional estimates is iteratively updated across the swarm, allowing the collective to localize and navigate with high reliability in the absence of centralized coordination.

The system maintains high positional confidence within the loaded area of operations. Nodes that exit the terrain boundary will experience progressive confidence degradation as observations diverge from the preloaded prior. This is a known constraint that defines the operational envelope, not a failure of the method. The approach is label-blind by design: the terrain prior is geographic, not object-classified. No feature-labeled dataset of the operating environment is required. The method was developed and validated in digital simulation and is designed for zero-shot generalization to physical deployment on low-cost edge hardware.

Motivation

Modern autonomous systems are overwhelmingly dependent on infrastructure they do not control. GPS can be denied, spoofed, or degraded. Communication links can be jammed or severed. Cloud-based coordination requires connectivity that cannot be guaranteed in contested, disaster, or remote operating environments.

The operational failure mode is not theoretical. It is the daily reality for systems deployed in environments like those currently documented in active conflict zones. A swarm that cannot localize without external reference is a swarm that cannot be trusted when it is needed most.

Key Contributions

• Inference-based localization using preloaded terrain data, with no GPS or live external signals required
• A label-blind positional confidence framework requiring no object-classified environmental data
• A Markov chain formulation for iterative confidence updating across distributed nodes
• Validation through physics-based digital simulation with design for zero-shot physical generalization
• A hardware target architecture built on low-cost accessible edge compute [23]

Relationship to Prior Work

This work builds directly on the body of research in aggressive autonomous flight [1], gradient-based local planning [2], and micro-robot swarm coordination [3]. The high-speed flight policies demonstrated by Loquercio et al. [4] established that competent navigation policies can be trained with lean neural networks operating on minimal sensor input, which informs the efficiency constraints of the approach presented here.

Visual SLAM methods including DROID-SLAM [8] and VINS-Mono [18] demonstrate the maturity of onboard localization without external reference. This work departs from visual SLAM in that it does not require camera-based feature tracking or a persistent map. The positional confidence model is derived from anomaly observation rather than feature correspondence.

The reinforcement learning framework follows the PPO formulation of Schulman et al. [14], with physics-based simulation informed by Liang and Lin [13]. The champion-level drone racing results of Kaufmann et al. [10] and the deep acrobatics work [11] establish the upper bound of what learned flight policies can achieve, motivating the pursuit of similar capability under infrastructure denial.

Status

Full methodology, experimental results, and implementation details remain restricted pending review. The citation record above represents the complete foundational literature from which this contribution was derived. All referenced authors are credited in full.