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Maximizing Persistent Coverage of a Predetermined Area of Interest by Swarms of Assets for Targets Acquisition and Engagement



OBJECTIVE: Design and develop an optimization algorithm to provide persistent coverage of an area of interest by swarms of assets for target acquisition and engagement. 

DESCRIPTION: This effort aims to provide a significant capability to the Soldier Lethality CFT by increasing data accuracy during the direct fire engagement process for digital soldiers. This topic will also develop swarming technologies that can be integrated into NGCV for targeting and engagement; specifically by utilizing augmentation of LOS/BLOS NGCV fires and effects integrated with upper-echelon systems for cooperative engagement, as well as the integration of fires and maneuver to achieve tactical overmatch. Current technologies should allow the development an optimized solution to cover a predetermined area of interest by the swarms of combined assets in the persistent way. In particular, this coverage will involve both mobile and stationary target acquisition and engagement assets. Some of these assets may be weaponized to engage the targets. The route patterns of moving assets can be assumed to be fixed in order to satisfy the persistent surveillance requirement. Also, periodic maintenance and refueling/recharging must be addressed. For stationary assets, the maintenance requirements may be significantly relaxed since the power consumption of these assets can be significantly reduced. The algorithm must also account for the possibility that assets may have the capability to fly and perch on the wall of the building, inside a cave or tunnel, on the trunk of a tree, or on other structures depending on the asset’s size, weight, and other factors. These assets might geolocate the targets and serve as potential forward observers for engaging the targets. The optimized solution should rely as much as possible on autonomy to enable assets capable to return to their place(s) of origin on with minimal communication both among assets with the operator unit controller (OUC). Special attention should also be paid to assure the collision avoidance among assets. 

PHASE I: Design and develop innovative state-of-the-art software optimization algorithm for the persistent coverage of a predetermined area of interest by the swarms of assets capable of autonomous navigation. Model the algorithm’s performance with data supply by assets with “fly and perch” capability. Demonstrate how the proposed algorithms will optimize the coverage provided by assets in a dynamic threat environment. 

PHASE II: Develop and demonstrate a prototype capability with swarms of at least six assets autonomously navigating over a predetermined area of interest using the developmental offline software algorithms. The collaborative assets should be demonstrate the capability of autonomous return to their points of origin. The prototype should demonstrate assets’ ability to transition to the forward observer state from the perching and dormant states based on the appropriate input triggers to initiate collaborative target engagement. This prototype should be capable of integrating with CCDC Armaments Center supplied fires and effects architecture. Conduct testing to demonstrate feasibility of this technology for operation within a simulation environment operated by CCDC Armaments Center. 

PHASE III: CCDC Armaments Center swarming/perching technology developed under this effort should have open architecture allowing it to be easily integrated with the tactical decision support systems and to enable swarming munition technologies. Department of Homeland security could use this capability to monitor the illegal crossings of the US borders. In addition, SOCOM could use this technology for surveillance of terrorist activities in urban places, while FBI/CIA could use it for intelligence gathering. 


1: Z.R. Bogdanowicz, "Swarm of autonomous unmanned aerial vehicles with 3D deconfliction", Proc. SPIE 10651, Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 1018, 106510L (9 May 2018), SPIE Defense + Security, 2018, Orlando, Florida. .

2:  Z.R. Bogdanowicz, "Flying swarm of drones over circulant digraph", IEEE Transactions on Aerospace and Electronic Systems, Vol. 53, No. 6, 2662-2670, 2017.

3:  Y. Cao, W. Yu, W. Ren, and G. Chen, "An overview of recent progress in the study of distributed multiagent coordination", IEEE Transactions on Industrial Informatics, Vol. 9, no. 1, 427-438, 2013.

4:  Z. Chen, M. C. Fan, and H. T. Zhang, "How much control is enough for network connectivity preservation and collision avoidance?", IEEE Transactions on Cybernetics, Vol. 45, No. 8, 1647-1656, 2015.

5:  B. Fidam, V. Gazi, S. Zhai, N. Cen, and E. Karatas, "Single-view distance-estimation-based formation control of robotic swarms", IEEE Transactions on Industrial Electronics, Vol. 60, No. 12, 5781-5791, 2013.

6:  J. H. Son and H. S. Ahn, "Formation coordination for propagation of group of mobile agents via self-mobile localization", IEEE Systems Journal, Vol. 9, No. 4, 1285-1296, 2015.

KEYWORDS: Perching, Autonomous UAV/UGV, Swarm Of UAVs/UGVs, Forward Observer, Target Engagement, Weaponized UAVs/UGVs 

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