Multi-Agent Navigation Using Anomaly Sensing in GPS-Denied Swarms
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Solution Overview
Problem
Existing navigation systems, such as GPS, are ineffective in GPS-denied environments, and current technologies face challenges in achieving accurate navigation without relying on external systems like landmark observations or GPS/GNSS, especially for mobile agents without network coordination.
Innovation Solution
The implementation of multi-agent navigation systems that utilize relative distance data, anomaly sensor data, and pre-surveyed map data to determine global pose and assign tasks based on specialized operational capabilities, allowing agents to navigate accurately without external systems by employing anomaly-aided navigation methods, including friendly spoofing and distributed anomaly field sensing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If each agent is equipped with independent anomaly sensing and IMU hardware to achieve accurate navigation in GPS-denied environments, then navigation accuracy is improved, but device complexity and SWaP+C increase
Solution Approach 1:
The system divides the anomaly sensing function across multiple agents, where only a subset of agents (first subset) are equipped with anomaly sensor subsystems. These specialized agents collect anomaly data that is then shared with the entire multi-agent system, eliminating the need for every agent to have redundant sensing hardware.
Solution Approach 2:
The anomaly data collected by the first subset of agents serves multiple purposes: it enables navigation for the entire multi-agent system, supports task assignment decisions, and provides environmental information for all agents regardless of whether they have their own sensors.
2Reliability
If all agents are equipped with anomaly sensor subsystems to navigate without external systems, then navigation reliability is improved, but manufacturing cost and device complexity increase
Solution Approach 1:
The system segments the sensing burden by assigning anomaly detection tasks only to a subset of agents. This reduces per-agent manufacturing costs while maintaining system-level reliability through distributed sensing and data sharing.
Solution Approach 2:
The system merges the sensing capabilities of multiple specialized agents into a shared resource that benefits the entire multi-agent system, achieving reliable navigation without requiring expensive equipment on every agent.
3Measurement precision
If GPS-based navigation is used, then navigation accuracy is improved, but the system becomes ineffective in GPS-denied environments
Solution Approach 1:
The multi-agent system becomes self-sufficient for navigation by using its own distributed anomaly sensors to collect and share environmental data, eliminating dependence on external GPS satellites while maintaining navigation capability in GPS-denied environments.
Solution Approach 2:
The system uses pre-surveyed map data as an intermediary reference framework. By comparing real-time anomaly sensor readings against the stored map, agents can determine their global pose without requiring direct connection to external positioning systems.
4Adaptability or versatility
If every agent has full sensing and navigation equipment, then individual agent capability is improved, but overall system SWaP+C increases
Solution Approach 1:
The system segments equipment requirements by creating specialized agents with specific functions. Only the first subset of agents carry anomaly sensors, while the second subset can have reduced equipment, optimizing the energy-weight profile across the system while maintaining overall capability.
Data Source
AI summary
Example computer-implemented methods and systems for anomaly-sensing based multi-agent navigation are disclosed. One example computer-implemented method includes: receiving relative distance data specifying distance between at least one pair of agents of a plurality of agents, each of a first subset of the plurality of agents having an anomaly sensor subsystem; determining a set of relative pose vectors based at least in part on the relative distance data; receiving anomaly data from at least one anomaly sensor subsystem of one of the plurality of agents, obtaining pre-surveyed map data; determining global pose data of the plurality of agents based on the relative distance data and based on comparing the anomaly data to the pre-surveyed map data; and assigning a task to at least one of the plurality of agents based at least in part on a specialized operational capability of the at least one of the plurality of agents.