A distributed multi-UAV path planning approach for 3D environments

Rasche, Christoph; Stern, Claudius; Kleinjohann, Lisa; Kleinjohann, Bernd

doi:10.1109/icara.2011.6144847

Cited by 17 publications

(8 citation statements)

References 5 publications

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“…Yang has evaluated the robustness of the system by flying over, flying beneath or flying through obstaclesusing doors and windows of a building. Rasche and Stern [3] applied the approach based on artificial potential fields and a gradient method to calculate paths, which ensures the multiple UAVs complete a fast exploration of unknown, partially or completely known environments consisting of complex objects. In terms of on-board vision system for determining obstacles and objects, Huang [1] developed a system for visual odometry and mapping applying an RGB-D camera which enables an autonomous flight in cluttered environments using only onboard sensor data.…”

Section: Introductionmentioning

confidence: 99%

A vision and GPS-based real-time trajectory planning for MAV in unknown urban environments

Flores

Zhou

Lozano

et al. 2013

2013 International Conference on Unmanned Aircraft Systems (ICUAS)

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This paper addresses the issue of real-time optimal trajectory generation of a micro Air Vehicle (MAV) in unknown urban environments. The MAV is required to navigate from an initial and outdoor position to a final position inside a building. To achieve this objective, we develop a safe path planning method using the information provided by the GPS and a consumer depth camera. With the purpose to develop a safe path planning with obstacle avoidance capabilities, a model predictive control approach is developed, which uses the environment information acquired by the navigation system.

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Section: Introductionmentioning

confidence: 99%

A vision and GPS-based real-time trajectory planning for MAV in unknown urban environments

Flores

Zhou

Lozano

et al. 2013

2013 International Conference on Unmanned Aircraft Systems (ICUAS)

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“…Currently, multi-UAV are widely deployed to explore locations that are difficult to reach and can quickly deliver goods over complex terrain [16]. During floods, earthquakes and other disasters, multi-UAV can investigate potential route hazards without being affected by road conditions.…”

Section: Introductionmentioning

confidence: 99%

A multi-UAV clustering strategy for reducing insecure communication range

Zou

Zhao

et al. 2019

Computer Networks

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Multi-unmanned aerial vehicle (UAV) flight formations can be deployed to monitor large areas. Individual UAVs communicate and exchange information while formation flying. but, such communication presents a security risk. The area between the UAV group range and the group communication range is called the insecurity range and in this region multi-UAV communication can cause serious information leakage. To resolve this problem, this paper considers two aspects, namely, cooperative control and secure communication. To implement cooperative control, a clustering algorithm is presented to accelerate the speed at which the multi-UAV formation converges. By setting the flight control factor to accelerate the convergence of multi-UAV, the UAV group forms a flock. To facilitate secure communication, the hierarchical virtual communication ring (HVCR) strategy is deployed to reduce the boundary of group communication and minimize the insecure range. The effectiveness of the clustering algorithm and HVCR strategy is demonstrated via theoretical analysis and experiments. In the case of 50 and 100 nodes, the results show that the clustering algorithm can facilitate multi-UAV group flocks. In the case of 25, 30, 35 and 40 nodes, the HVCR strategy can reduce the relative size of the insecure range to 65.33%, 62.95%, 61.50% and 60.55%, respectively.

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“…However, faced with the complex flight environment, trajectory planning in 2-D cannot meet the mission requirements of the UAVs. So the path planning algorithms in 3-D space have been paid more attention by many researchers [8][9][10][11]. The conventional path planning methods for UAVs in 3-D only take the height information as an additional consideration in the cost function [8], so the optimal trajectory cannot be obtained.…”

Section: Introductionmentioning

confidence: 99%

Optimal flight path planning for UAVs in 3-D threat environment

Zhang

2014

2014 International Conference on Unmanned Aircraft Systems (ICUAS)

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Nowadays, the environments surrounding modern battlefield are becoming increasingly complicated, since the threats are not only from the ground but also from the sky. UAV with reconnaissance mission will take more risk when flying along an improper planned path, so path planning of UAV in complex 3-D environments is very significant and challenging. Aimed at the problem, this paper proposes a novel optimal path planning method for UAV based on the flight space partitioning, Dijkstra algorithm and potential field theory. Specifically, under the cases that the locations of threats are assumed to be known and the whole flight space is partitioned into a number of cells and each cell has a safest node. Then, a 3-D network is formed by connecting the nodes of adjacent cells and a shortest suboptimal path is marked on the network with Dijkstra algorithm. Finally, the optimal path is obtained with artificial potential field method. To verify the proposed algorithm, simulation results in two cases are shown.

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A distributed multi-UAV path planning approach for 3D environments

Cited by 17 publications

References 5 publications

A vision and GPS-based real-time trajectory planning for MAV in unknown urban environments

A vision and GPS-based real-time trajectory planning for MAV in unknown urban environments

A multi-UAV clustering strategy for reducing insecure communication range

Optimal flight path planning for UAVs in 3-D threat environment

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