scholarly journals Occlusion-Aware UAV Path Planning for Reconnaissance and Surveillance

Drones ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 98
Author(s):  
Jian Zhang ◽  
Hailong Huang
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Real Time ◽  
Traveling Salesman Problem ◽  
Search And Rescue ◽  
Traveling Salesman ◽  
Complex Environments ◽  
Aerial Vehicles ◽  
Wide Range ◽  
The Traveling Salesman Problem

Unmanned Aerial Vehicles (UAVs) have become necessary tools for a wide range of activities including but not limited to real-time monitoring, surveillance, reconnaissance, border patrol, search and rescue, civilian, scientific and military missions, etc. Their advantage is unprecedented and irreplaceable, especially in environments dangerous to humans, for example, in radiation or pollution-exposed areas. Two path-planning algorithms for reconnaissance and surveillance are proposed in this paper, which ensures every point on the target ground area can be seen at least once in a complete surveillance circle. Moreover, the geometrically complex environments with occlusions are considered in our research. Compared with many existing methods, we decompose this problem into a waypoint-determination problem and an instance of the traveling-salesman problem.

Speeding Up On-Line Route Scheduling for an Autonomous Robot Through Pre-Built Paths

Robotica ◽  
2020 ◽  
pp. 1-13
Author(s):  
Raul Alves ◽  
Josué Silva de Morais ◽  
Keiji Yamanaka
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Indoor Environments ◽  
On Line ◽  
Real Time Applications ◽  
The Traveling Salesman Problem ◽  
Route Scheduling

SUMMARY Today, robots can be found helping humans with their daily tasks. Some tasks require the robot to visit a set of locations in the environment efficiently, like in the Traveling Salesman Problem. As indoor environments are maze-like areas, feasible paths connecting locations must be computed beforehand, so they can be combined during the scheduling, which can be impracticable for real-time applications. This work presents an on-line Route Scheduling supported by a Fast Path Planning Method able to adjust pre-built paths. Experiments were carried out with virtual and real robots to evaluate time and quality of tours.

scholarly journals Autonomous Unmanned Aerial Vehicles in Search and Rescue Missions Using Real-Time Cooperative Model Predictive Control

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4067 ◽  
Author(s):  
Fabio A. A. Andrade ◽  
Anthony Hovenburg ◽  
Luciano Netto de de Lima ◽  
Christopher Dahlin Rodin ◽  
Tor Arne Johansen ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Unmanned Aerial Vehicles ◽  
Real Time ◽  
Predictive Control ◽  
Kinematic Model ◽  
Search And Rescue ◽  
International Standards ◽  
Area Of Interest ◽  
Aerial Vehicles ◽  
Human Operators

Unmanned Aerial Vehicles (UAVs) have recently been used in a wide variety of applications due to their versatility, reduced cost, rapid deployment, among other advantages. Search and Rescue (SAR) is one of the most prominent areas for the employment of UAVs in place of a manned mission, especially because of its limitations on the costs, human resources, and mental and perception of the human operators. In this work, a real-time path-planning solution using multiple cooperative UAVs for SAR missions is proposed. The technique of Particle Swarm Optimization is used to solve a Model Predictive Control (MPC) problem that aims to perform search in a given area of interest, following the directive of international standards of SAR. A coordinated turn kinematic model for level flight in the presence of wind is included in the MPC. The solution is fully implemented to be embedded in the UAV on-board computer with DUNE, an on-board navigation software. The performance is evaluated using Ardupilot’s Software-In-The-Loop with JSBSim flight dynamics model simulations. Results show that, when employing three UAVs, the group reaches 50% Probability of Success 2.35 times faster than when a single UAV is employed.

A real-time field experiment on search and rescue operations assisted by unmanned aerial vehicles

2018 ◽  
Vol 35 (6) ◽  
pp. 906-920 ◽  
Author(s):  
Tomasz Niedzielski ◽  
Mirosława Jurecka ◽  
Bartłomiej Miziński ◽  
Joanna Remisz ◽  
Jacek Ślopek ◽  
...  
Keyword(s):  
Field Experiment ◽  
Unmanned Aerial Vehicles ◽  
Real Time ◽  
Search And Rescue ◽  
Aerial Vehicles

scholarly journals Distributed Detect-and-Avoid for Multiple Unmanned Aerial Vehicles in National Air Space

2019 ◽  
Vol 141 (7) ◽  
Author(s):  
Mohammad Sarim ◽  
Mohammadreza Radmanesh ◽  
Matthew Dechering ◽  
Manish Kumar ◽  
Ravikumar Pragada ◽  
...  
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Real Time ◽  
Precision Agriculture ◽  
Traffic Management ◽  
Mixed Integer ◽  
Dynamic Constraints ◽  
Aerial Vehicles ◽  
Package Delivery ◽  
Feasible Solutions

Small unmanned aerial vehicles (UAVs) have the potential to revolutionize various applications in civilian domain such as disaster management, search and rescue operations, law enforcement, precision agriculture, and package delivery. As the number of such UAVs rise, a robust and reliable traffic management is needed for their integration in national airspace system (NAS) to enable real-time, reliable, and safe operation. Management of UAVs traffic in NAS becomes quite challenging due to issues such as real-time path planning of large number of UAVs, communication delays, operational uncertainties, failures, and noncooperating agents. In this work, we present a novel UAV traffic management (UTM) architecture that enables the integration of such UAVs in NAS. A combined A*–mixed integer linear programming (MILP)-based solution is presented for initial path planning of multiple UAVs with individual mission requirements and dynamic constraints. We also present a distributed detect-and-avoid (DAA) algorithm based on the concept of resource allocation using a market-based approach. The results demonstrate the scalability, optimality, and ability of the proposed approach to provide feasible solutions that are versatile in dynamic environments.

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