Multi-UAV Active Sensing with Information Gain-based Planning and Belief Fusion

2026-06-09

AI Summary

This study addresses the challenge of active environmental mapping for multiple UAVs under constraints of limited endurance, perceptual uncertainty, and trade-offs between coverage efficiency and reconstruction accuracy, with a focus on precision agriculture. The authors propose a collaborative perception framework grounded in probabilistic belief maps, integrating an information gain–based informative path planning (IGbIPP) strategy with factor graph–based modeling of spatial dependencies. Three belief fusion schemes—Bayesian, log-odds, and Dempster–Shafer—are systematically evaluated. Experimental results demonstrate that IGbIPP substantially reduces map entropy and reconstruction error; wide-field-of-view observations enhance both coverage efficiency and mapping accuracy; fixed or biased spatial weighting outperforms adaptive mechanisms; and the proposed fusion approach achieves state-of-the-art collaborative mapping performance.

Abstract

Unmanned aerial vehicles (UAVs) are increasingly used for active sensing and information gathering in spatially distributed environments. Their performance, however, is constrained by limited flight time, sensing uncertainty, and the trade-off between spatial coverage and observation accuracy. This paper presents a real-world validation of a multi-UAV active sensing framework for probabilistic binary terrain mapping, with precision agriculture used as the application case. The environment is represented as a probabilistic belief map, where spatial dependencies are modeled through a factor-graph formulation. UAV decision making is guided by Information Gain based Informative Path Planning (IGbIPP), and the approach is compared with Random Walk and Sweep coverage path planning baselines using both synthetic terrains and real UAV-derived agricultural imagery. The study also evaluates spatial correlation weights and several probabilistic belief-fusion rules for multi-UAV information sharing. Results show that IGbIPP reduces entropy and mapping error more effectively than the baselines, while a wider field of view improves real-world coverage and map accuracy. The results further show that simple equal or biased spatial weights can be more robust than adaptive weights, and that Bayesian, log-odds, and Dempster--Shafer fusion achieve the best cooperative mapping performance. These findings highlight the importance of uncertainty-driven planning, sensing geometry, spatial modeling, and probabilistic fusion for real-world UAV-based active sensing.

Problem

• multi-UAV
• active sensing
• information gain
• belief fusion
• terrain mapping

Innovation

• Information Gain-based Planning
• Belief Fusion
• Factor Graph
• Multi-UAV Coordination
• Probabilistic Terrain Mapping