scholarly journals Towards Flocking Navigation and Obstacle Avoidance for Multi-UAV Systems through Hierarchical Weighting Vicsek Model

Aerospace ◽  
2021 ◽  
Vol 8 (10) ◽  
pp. 286
Author(s):  
Xingyu Liu ◽  
Chao Yan ◽  
Han Zhou ◽  
Yuan Chang ◽  
Xiaojia Xiang ◽  
...  
Keyword(s):  
Obstacle Avoidance ◽  
High Performance ◽  
Local Information ◽  
Convergence Time ◽  
Complex Environments ◽  
System Safety ◽  
Vicsek Model ◽  
Aerial Vehicle ◽  
Weighting Mechanism ◽  
Multi Uav

Flocking navigation and obstacle avoidance in complex environments remain challenging for multiple unmanned aerial vehicle (multi-UAV) systems, especially when only one UAV (termed as information UAV) knows the predetermined path and the communication range is limited. To this end, we propose a hierarchical weighting Vicsek model (HWVEM). In this model, a hierarchical weighting mechanism and an obstacle avoidance mechanism are designed. Based on the hierarchical weighting mechanism, all the UAVs are divided into different layers, and assigned with different weights according to the layer to which they belong. The purpose is to align the rest of UAVs with the information UAV more efficiently. Subsequently, the obstacle avoidance mechanism that utilizes only the local information is developed to ensure the system safety in an environment filled with obstacles differing in size and shape. A series of simulations have been conducted to demonstrate the high performance of HWVEM in terms of convergence time, success rate, and safety.

Multi-UAV Cooperative Task Planning for Border Patrol based on Hierarchical Optimization

2021 ◽  
Author(s):  
Hai-shi Liu ◽  
Yu-xuan Sun ◽  
Nan Pan ◽  
Qi-yong Chen ◽  
Xiao-jue Guo ◽  
...  
Keyword(s):  
Chaos Theory ◽  
Hierarchical Optimization ◽  
Border Patrol ◽  
Complex Environments ◽  
Environmental Model ◽  
Other Information ◽  
Aerial Vehicle ◽  
Scene Information ◽  
Simulation Results ◽  
Multi Uav

In order to improve the patrol efficiency of border patrol drones, based on unmanned aerial vehicle (UAV) border patrol missions in multiple complex environments, this article proposes a whale algorithm based on chaos theory to plan patrol missions for multiple drones. First, according to the terrain the corresponding environmental model is established for the topography and then solved in layers to obtain the number of drones and other information that each base needs to send to the patrol area. Further, the use of drones with cameras and other detection equipment to patrol the scene information and images extract and transfer to the terminal in real time, and further detect suspicious persons and vehicles on the screen. The final simulation results show that the proposed scheme can be effectively applied to the planning of multi-UAV coordinated missions for border patrol.

Three-Dimensional Obstacle Avoidance of Blimp-Type Unmanned Aerial Vehicle Flying in Unknown and Non-Uniform Wind Disturbance

2007 ◽  
Vol 19 (2) ◽  
pp. 166-173 ◽  
Author(s):  
Hiroshi Kawano ◽  
Keyword(s):  
Motion Planning ◽  
Obstacle Avoidance ◽  
Unmanned Aerial Vehicle ◽  
High Performance ◽  
Three Dimensional ◽  
Longitudinal Motion ◽  
Wind Disturbance ◽  
Flow Disturbances ◽  
Markov Decision ◽  
Aerial Vehicle

A blimp-type unmanned aerial vehicle (BUAV) maintains its longitudinal motion using buoyancy provided by the air around it. This means the density of a BUAV equals that of the surrounding air. Because of this, the motion of a BUAV is seriously affected by flow disturbances, whose distribution is usually non-uniform and unknown. In addition, the inertia in the heading motion is very large. There is also a strict limitation on the weight of equipment in a BUAV, so most BUAVs are so-called under-actuated robots. From this situation, it can be said that the motion planning of the BUAV considering the stochastic property of the disturbance is needed for obstacle avoidance. In this paper, we propose an approach to the motion planning of a BUAV via the application of Markov decision process (MDP). The proposed approach consists of a method to prepare a discrete MDP model of the BUAV motion and a method to maintain the effect of the unknown wind on the BUAV’s motion. A dynamical simulation of a BUAV in an environment with wind disturbance shows high performance of the proposed method.

scholarly journals Hierarchical Weighting Vicsek Model for Flocking Navigation of Drones

Drones ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 74
Author(s):  
Xingyu Liu ◽  
Xiaojia Xiang ◽  
Yuan Chang ◽  
Chao Yan ◽  
Han Zhou ◽  
...  
Keyword(s):  
Collision Avoidance ◽  
Obstacle Avoidance ◽  
Path Following ◽  
Convergence Speed ◽  
Regulation Mechanism ◽  
Simulation Experiments ◽  
Interaction Radius ◽  
Vicsek Model ◽  
Weighting Mechanism

Flocking navigation, involving alignment-guaranteed path following and collision avoidance against obstacles, remains to be a challenging task for drones. In this paper, we investigate how to implement flocking navigation when only one drone in the swarm masters the predetermined path, instead of all drones mastering their routes. Specifically, this paper proposes a hierarchical weighting Vicsek model (WVEM), which consists of a hierarchical weighting mechanism and a layer regulation mechanism. Based on the hierarchical mechanism, all drones are divided into three layers and the drones at different layers are assigned with different weights to guarantee the convergence speed of alignment. The layer regulation mechanism is developed to realize a more flexible obstacle avoidance. We analyze the influence of the WVEM parameters such as weighting value and interaction radius, and demonstrate the flocking navigation performance through a series of simulation experiments.

scholarly journals Information Distribution in Multi-Robot Systems: Utility-Based Evaluation Model

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 710 ◽  
Author(s):  
Michał Barciś ◽  
Agata Barciś ◽  
Hermann Hellwagner
Keyword(s):  
Evaluation Model ◽  
Proof Of Concept ◽  
Information Distribution ◽  
Unified Framework ◽  
Robot Operating System ◽  
Robot Systems ◽  
System 2 ◽  
Aerial Vehicle ◽  
Multi Uav ◽  
Multi Robot

This work addresses the problem of information distribution in multi-robot systems, with an emphasis on multi-UAV (unmanned aerial vehicle) applications. We present an analytical model that helps evaluate and compare different information distribution schemes in a robotic mission. It serves as a unified framework to represent the usefulness (utility) of each message exchanged by the robots. It can be used either on its own in order to assess the information distribution efficacy or as a building block of solutions aimed at optimizing information distribution. Moreover, we present multiple examples of instantiating the model for specific missions. They illustrate various approaches to defining the utility of different information types. Finally, we introduce a proof of concept showing the applicability of the model in a robotic system by implementing it in Robot Operating System 2 (ROS 2) and performing a simple simulated mission using a network emulator. We believe the introduced model can serve as a basis for further research on generic solutions for assessing or optimizing information distribution.

scholarly journals Vision-Based Obstacle Avoidance Strategies for MAVs Using Optical Flows in 3-D Textured Environments

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2523 ◽  
Author(s):  
Gangik Cho ◽  
Jongyun Kim ◽  
Hyondong Oh
Keyword(s):  
Cost Effectiveness ◽  
Optical Flow ◽  
Obstacle Avoidance ◽  
Weighting Function ◽  
Light Weight ◽  
Complex Environments ◽  
Aerial Vehicles ◽  
Focus Of Expansion ◽  
Heading Direction ◽  
Optical Flows

Due to payload restrictions for micro aerial vehicles (MAVs), vision-based approaches have been widely studied with their light weight characteristics and cost effectiveness. In particular, optical flow-based obstacle avoidance has proven to be one of the most efficient methods in terms of obstacle avoidance capabilities and computational load; however, existing approaches do not consider 3-D complex environments. In addition, most approaches are unable to deal with situations where there are wall-like frontal obstacles. Although some algorithms consider wall-like frontal obstacles, they cause a jitter or unnecessary motion. To address these limitations, this paper proposes a vision-based obstacle avoidance algorithm for MAVs using the optical flow in 3-D textured environments. The image obtained from a monocular camera is first split into two horizontal and vertical half planes. The desired heading direction and climb rate are then determined by comparing the sum of optical flows between half planes horizontally and vertically, respectively, for obstacle avoidance in 3-D environments. Besides, the proposed approach is capable of avoiding wall-like frontal obstacles by considering the divergence of the optical flow at the focus of expansion and navigating to the goal position using a sigmoid weighting function. The performance of the proposed algorithm was validated through numerical simulations and indoor flight experiments in various situations.

Multi-UAV Specification and Control with a Single Pilot-in-the-Loop

2020 ◽  
Vol 08 (04) ◽  
pp. 269-277
Author(s):  
Patricio Moreno ◽  
Santiago Esteva ◽  
Ignacio Mas ◽  
Juan I. Giribet
Keyword(s):  
Remote Control ◽  
Unmanned Aerial Vehicles ◽  
Field Experiments ◽  
Aerial Vehicles ◽  
Simulated Environment ◽  
Aerial Vehicle ◽  
Space Formulation ◽  
Trajectory Following ◽  
And Control ◽  
Multi Uav

This work presents a multi-unmanned aerial vehicle formation implementing a trajectory-following controller based on the cluster-space robot coordination method. The controller is augmented with a feed-forward input from a control station operator. This teleoperation input is generated by means of a remote control, as a simple way of modifying the trajectory or taking over control of the formation during flight. The cluster-space formulation presents a simple specification of the system’s motion and, in this work, the operator benefits from this capability to easily evade obstacles by means of controlling the cluster parameters in real time. The proposed augmented controller is tested in a simulated environment first, and then deployed for outdoor field experiments. Results are shown in different scenarios using a cluster of three autonomous unmanned aerial vehicles.

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