Multirotor aircraft formation flight control with collision avoidance capability

Angelis, De; Giulietti, Fabrizio; Rossetti, Gianluca

doi:10.1016/j.ast.2018.04.002

Cited by 28 publications

(3 citation statements)

References 28 publications

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“…Reference [9] described a track guidance method for formation flight of UAVs. Study [10] presented a multirotor aircraft formation flight control method with collision avoidance capability. Reference [11] presented a coordinated flight control method for hypersonic glide vehicles with multiple no-fly-zone constraints.…”

Section: Introductionmentioning

confidence: 99%

Formation Generation for Multiple Unmanned Vehicles Using Multi-Agent Hybrid Social Cognitive Optimization Based on the Internet of Things

Yao

Wen

2019

Sensors

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Multi-agent hybrid social cognitive optimization (MAHSCO) based on the Internet of Things (IoT) is suggested to solve the problem of the generation of formations of unmanned vehicles. Through the analysis of the unmanned vehicle formation problem, formation principles, formation scale, unmanned vehicle formation safety distance, and formation evaluation indicators are taken into consideration. The application of the IoT enables the optimization of distributed computing. To ensure the reliability of the formation algorithm, the convergence of MAHSCO has been proved. Finally, computer simulation and actual unmanned aerial vehicle (UAV) formation generation flight generating four typical formations are carried out. The result of the actual UAV formation generation flight is consistent with the simulation experiment, and the algorithm performs well. The MAHSCO algorithm based on the IoT is proved to be able to generate formations that meet the mission requirements quickly and accurately.

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Section: Introductionmentioning

confidence: 99%

Formation Generation for Multiple Unmanned Vehicles Using Multi-Agent Hybrid Social Cognitive Optimization Based on the Internet of Things

Yao

Wen

2019

Sensors

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“…Nonetheless, the possibility of Beyond-Visual-Line-of-Sight (BVLOS) operations has been studied and demonstrated in tests performed by industry and academia, thanks to the combined use of advanced control systems and filtering techniques [2,3], where virtual and augmented reality can be adopted to increase the level of situational awareness of a remote pilot [4]. Demanding autonomous tasks, such as the use of a fleet of autonomous vehicles, with collision avoidance capabilities, have also been studied [5].…”

Section: Introductionmentioning

confidence: 99%

Optimal Sizing of Electric Multirotor Configurations

et al. 2018

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A sizing tool for the definition of the configuration of electrically powered multirotor platforms is developed, which accounts for a realistic battery discharge model. The tool is developed to provide the community with the possibility of deriving the best configuration for performing a given task, while accounting for specific constraints and performance requirements. An evolutionary algorithm is used for searching the design space and to identify feasible designs with optimal performance in terms of maximum hovering time on the target and payload weight fraction.

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“…Several theoretical estimation algorithms [1], [2] and design tools [3], [4] have been available for optimizing performances and many functionalities in that area are still under development. In order to increase safety and flexibility of use of these machines, it is necessary to expand their motion planning capabilities in complex and difficult environments adopting new strategies of autonomous navigation and control [5]. This paper presents a novel approach to RPAS navigation in scenarios where collision avoidance with obstacles and other vehicles is a mandatory task.…”

Section: Introductionmentioning

confidence: 99%

Optimal autonomous quadrotor navigation in an obstructed space

Giulietti

Pipeleers

Rossetti

et al. 2017

2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS)

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This paper presents an ambitious methodology of autonomous navigation for multirotor UAVs in obstructed environments. The strategy was formulated to provide the multirotor vehicles the capability to produce autonomously quasi-optimal and safe trajectories, although generally they have at their disposal limited computational resources on board. The problem is formulated in a model predictive control (MPC) architecture in which motion planning and trajectory tracking processes are solved separately as if they were stored in two different devices. The first process uses a spline-based motion planning approach to generate smooth and safe trajectories. At this step also a multirotor's simpified dynamic model and environment information are taken into account. The second process uses trajectory inputs, which are total thrust and attitude angle rates, to steer the multirotor during the flight. Both adequate time horizon and update frequency are chosen in order to account for disturbances and dynamics model mismatch. The methodology is validated by simulations and future work will include experimental tests in outdoor environment.

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Multirotor aircraft formation flight control with collision avoidance capability

Cited by 28 publications

References 28 publications

Formation Generation for Multiple Unmanned Vehicles Using Multi-Agent Hybrid Social Cognitive Optimization Based on the Internet of Things

Formation Generation for Multiple Unmanned Vehicles Using Multi-Agent Hybrid Social Cognitive Optimization Based on the Internet of Things

Optimal Sizing of Electric Multirotor Configurations

Optimal autonomous quadrotor navigation in an obstructed space

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