More Like this

1. DESIGN RESEARCH BASED DEVELOPMENT OF CAMOUFLAGE LANDSCAPE FEATURES TO PREVENT CRIMINAL UAV ACTIVITY

   Hewitt, Robert; Nassar, Hala; Cummings, Mary (January 2019, Landscape research record)

   The popularity of small consumer drones (UAVs) has prompted increased use of these vehicles in and around public outdoor spaces, with expected commercial drone numbers to reach nearly 7 million by 2030 (FAA). Research in landscape architecture related to UAV (drone) use in public space to date, has just begun to address conceptual approaches to landscape assessment, representation, and park user behavior (Kullmann, Park). While research related to the development of countermeasures for security purposes is more extensive, no research to date addresses the development of landscape countermeasures for the use of UAVs in criminal activities. The paper presents design-based research (DBR) methodology and findings funded by a multiyear, multidisciplinary NSF grant to develop landscape architectural interventions that discourage the use of UAVs for criminal purposes at correctional facilities. Consistent with design based research (DBR) models (Brown), this project is complex, incorporating the development of a) landscape assessments for potential UAV launch and landing sites around prisons; b) the creation of UAV tracking and monitoring systems, and c) the development of model countermeasures. The paper describes design and placement of embedded landscape features utilizing landscape camouflage principles for UAV detection systems in forested upstate North Carolina. Modelled camouflage mimicked landscape features and were fabricated in two stages: 1) landscape superstructure, and 2) landscape camouflage. The embedded landscape features incorporated a launch warning system capable of alerting prison officials of drone launch locations, identifying future drone operators, and predicting drone flight paths. Keywords: 

   more » « less
2. DESIGN RESEARCH BASED DEVELOPMENT OF CAMOUFLAGE LANDSCAPE FEATURES TO PREVENT CRIMINAL UAV ACTIVITY

   Hewitt, Robert and (January 2019, Landscape research record)

   The popularity of small consumer drones (UAVs) has prompted increased use of these vehicles in and around public outdoor spaces, with expected commercial drone numbers to reach nearly 7 million by 2030 (FAA). Research in landscape architecture related to UAV (drone) use in public space to date, has just begun to address conceptual approaches to landscape assessment, representation, and park user behavior (Kullmann, Park). While research related to the development of countermeasures for security purposes is more extensive, no research to date addresses the development of landscape countermeasures for the use of UAVs in criminal activities. The paper presents design-based research (DBR) methodology and findings funded by a multiyear, multidisciplinary NSF grant to develop landscape architectural interventions that discourage the use of UAVs for criminal purposes at correctional facilities. Consistent with design based research (DBR) models (Brown), this project is complex, incorporating the development of a) landscape assessments for potential UAV launch and landing sites around prisons; b) the creation of UAV tracking and monitoring systems, and c) the development of model countermeasures. The paper describes design and placement of embedded landscape features utilizing landscape camouflage principles for UAV detection systems in forested upstate North Carolina. Modelled camouflage mimicked landscape features and were fabricated in two stages: 1) landscape superstructure, and 2) landscape camouflage. The embedded landscape features incorporated a launch warning system capable of alerting prison officials of drone launch locations, identifying future drone operators, and predicting drone flight paths. 

   more » « less
3. Visual pose estimation of USV from UAV to assist drowning victims recovery

   https://doi.org/10.1109/SSRR.2016.7784291  

   Dufek, Jan; Murphy, Robin (October 2016, Safety, Security, and Rescue Robotics (SSRR), 2016 IEEE International Symposium on)

   This paper presents the visual localization subsystem of the ongoing EMILY project. This project aims to assist responders with establishing contact with drowning victims as quickly as possible through the use of a 1.3-meter autonomous unmanned surface vehicle (USV). Once victims are reached, the device can serve as a flotation device to support them. An unmanned aerial vehicle (UAV) provides a live video feed to responders and aids USV's navigation by visually estimating the USV's position and orientation. The movement of the UAV and the USV along with varying lighting, distance, and perspective make the task of estimating USV's position and orientation challenging. We present two implemented solutions for reliable visual localization, the first relying on color thresholding and contour detection, and the second using histograms, back-projection, and CamShift algorithm. The visual localization system was validated in four proof-of-concept field trials. Other unsuccessful methods are discussed. 

   more » « less
4. Autonomous UAV with Learned Trajectory Generation and Control

   https://doi.org/10.1109/SiPS47522.2019.9020508  

   Li, Yilan; Li, Mingyang; Sanyal, Amit; Wang, Yanzhi; Qiu, Qinru (October 2019, IEEE Workshop on Signal Processing Systems (SIPS))

   Unmanned aerial vehicle (UAV) technology is a rapidly growing field with tremendous opportunities for research and applications. To achieve true autonomy for UAVs in the absence of remote control, external navigation aids like global navigation satellite systems and radar systems, a minimum energy trajectory planning that considers obstacle avoidance and stability control will be the key. Although this can be formulated as a constrained optimization problem, due to the complicated non-linear relationships between UAV trajectory and thrust control, it is almost impossible to be solved analytically. While deep reinforcement learning is known for its ability to provide model free optimization for complex system through learning, its state space, actions and reward functions must be designed carefully. This paper presents our vision of different layers of autonomy in a UAV system, and our effort in generating and tracking the trajectory both using deep reinforcement learning (DRL). The experimental results show that compared to conventional approaches, the learned trajectory will need 20% less control thrust and 18% less time to reach the target. Furthermore, using the control policy learning by DRL, the UAV will achieve 58.14% less position error and 21.77% less system power. 

   more » « less
5. Path Planning for Indoor UAV Using A* and Late Acceptance Hill Climbing Algorithms Utilizing Probabilistic Roadmap

   https://doi.org/10.14419/ijet.v9i4.31033  

   Hopkins, Jacob; Joy, Forrest; Sheta, Alaa; Turabieh, Hamza; Kar, Dulal (October 2020, International Journal of Engineering & Technology)

   null (Ed.)

   The main objective of an unmanned aerial vehicle (UAV) path planning is to generate a flight path that links a start point to an endpoint in an indoor space avoiding obstacles.  Path planning is essential for many real-life applications such as an autonomous car, surveillance mission, farming robots, unmanned aerial vehicles package delivery, space exploration, and many others. To create an optimal path, we need to adopt a specific criterion to minimize the distance the UAV must travel such as the Euclidean distance. In this paper, we provide our initial idea of creating an optimal path for indoor UAV using both A* and the Late Acceptance Hill Climbing (LAHC) algorithms. We are adopting an indoor search environment with various complexity and utilize the Probabilistic Roadmap algorithm (PRM) as a search space for both algorithms. The basic idea following PRM is to generate random sample points in the space and search these points for an optimal path. The developed results show that the LAHC algorithm outperforms the A* algorithm. 

   more » « less