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Unmanned aerial vehicles (UAVs) can collaborate as teams to accomplish diverse mission objectives, such as target search and tracking. This paper introduces a method that leverages accumulated target-density information over the course of a UAV mission to adapt path-planning rewards, guiding UAVs toward areas with a higher likelihood of target presence. The target density is modeled using a Gaussian process, which is iteratively updated as the UAVs search the environment. Unlike conventional search algorithms that prioritize unexplored regions, this approach incentivizes revisiting target-rich areas. The target-density information is shared across UAVs using decentralized consensus filters, enabling cooperative path selection that balances the exploration of uncertain regions with the exploitation of known high-density areas. The framework presented in this paper provides an adaptive cooperative search method that can quickly develop an understanding of the region’s target-dense areas, helping UAVs refine their search. Through Monte Carlo simulations, we demonstrate this method in both a 2D grid region and road networks, showing up to a 26% improvement in target density estimates. 

Unmanned aerial systems (UAS) are effective forsurveillance and monitoring, but struggle with persistent, long-term tracking due to limited flight time. Persistent trackingcan be accomplished using multiple vehicles if one vehiclecan effectively hand off the tracking information to anotherreplacement vehicle. In this paper we propose a solution tothe moving-target handoff problem in the absence of GPS. Theproposed solution uses a nonlinear complimentary filter forself-pose estimation using only an IMU, a particle filter forrelative pose estimation between UAS using a relative rangemeasurement, visual target tracking using a gimballed camerawhen the target is close to the handoff UAS, and track correlationlogic using Procrustes analysis to perform the final target handoffbetween vehicles. We present extensive simulation results thatdemonstrates the effectiveness of our approach and performMonte-Carlo simulations that indicate a 97% successful handoffrate using the proposed methods. 

Small rotorcraft unmanned air vehicles (sUAVs) are valuable tools in solving geospatial inspection challenges. One area where this is being widely explored is disaster reconnaissance [1]. Using sUAVs to collect images provides engineers and government officials critical information about the conditions before and after a disaster [2]. This is accomplished by creating high- fidelity 3D models from the sUAV’s imagery. However, using an sUAV to perform inspections is a challenging task due to constraints on the vehicle’s flight time, computational power, and data storage capabilities [3]. The approach presented in this article illustrates a method for utilizing multiple sUAVs to inspect a disaster region and merge the separate data into a single high-resolution 3D model. 

To grant unmanned aerial vehicles (UAVs) greater access to the National Airspace System (NAS), a reliable system to detect and track them must be established. This paper combines multiple radar systems into a single network to provide tracking of UAVs across a wide area. Each radar detects the UAV’s path and those detections are combined using a recursive random sample consensus (R-RANSAC) algorithm. Outdoor flight experiments show the ability of the system 

Teams of unmanned vehicles are capable of accomplishing a wide variety of mission objectives, such as searching for and tracking targets. In this paper, a receding horizon control is utilized with information based reward measures to accomplish these two competing mission objectives. This approach for cooperatively searching and tracking has proven to be effective in past work. However, it is not generally scalable for large numbers of vehicles due to the computational expense required when generating joint path decisions. This paper proposes a method to dynamically group vehicles with neighbors that have intersecting decision spaces, thus reducing computational cost while still maintaining reasonable performance. Each vehicle also decides its ideal event horizon based upon inferred knowledge of the operational environment, further reducing cost.