Web-like obstacles,such as safety nets,represent a unique hazard for drones,and especially UAVs(Unmanned Aerial Vehicles).Fencing and netting are particularly difficult to distinguish from the background using either ...Web-like obstacles,such as safety nets,represent a unique hazard for drones,and especially UAVs(Unmanned Aerial Vehicles).Fencing and netting are particularly difficult to distinguish from the background using either computer vision,lidar and sonar.In contrast,animals such as flying insects may detect these web-like obstacles using Optic Flow(OF),and more precisely motion parallax.A netting-avoidance solution was proposed using a OF-based detection method.The netting detection method was based on a signature defined by the shape of the OF magnitude across the visual field.We established that the OF shape depends on the orientation of the netting in relation to the hexarotor’s movement.This paper demonstrates netting detection in real-world experiments,according to any direction flight made by the UAV along the net.The proposed NOWA method(which stands for Netting Optical floW-based distinction Algorithm)separates the OF signatures belonging to these different surfaces-netting or background-whatever their orientations.By extracting the OF signatures of these different surfaces and separating them,the proposed visual method can estimate their relative locations and orientations.In a robotic simulations,the multirotor explores and navigates automatically using this netting detection method,using saccades to avoid obstacles.In the simulations,these saccades are also used to simplify netting detection by orienting itself systematically parallel to these planes,a behavior reminiscent of flying insects.展开更多
In this paper, the TAS-I （Thales Alenia Space-Italy） Test Bench for Robotics and Autonomy （TBRA） is presented. It is based on a flexible and modular software architecture （Framework Engine）, in which each functi...In this paper, the TAS-I （Thales Alenia Space-Italy） Test Bench for Robotics and Autonomy （TBRA） is presented. It is based on a flexible and modular software architecture （Framework Engine）, in which each functional module （representing the GNC subsystems） implements a key functionality of the GNC （Guidance Navigation and Control）. Modules communicate by means of standardised interfaces designed for exchange of necessary information among the modules composing the entire system. This approach permits the interchange-ability of each subsystem without affecting the overall functionalities of the GNC system. In this paper, the TBRA system, together with the implemented functional modules will be described. Tests results will be reported and future development will be discussed.展开更多
Background In recent decades,unmanned aerial vehicles(UAVs)have developed rapidly and been widely applied in many domains,including photography,reconstruction,monitoring,and search and rescue.In such applications,one ...Background In recent decades,unmanned aerial vehicles(UAVs)have developed rapidly and been widely applied in many domains,including photography,reconstruction,monitoring,and search and rescue.In such applications,one key issue is path and view planning,which tells UAVs exactly where to fly and how to search.Methods With specific consideration for three popular UAV applications(scene reconstruction,environment exploration,and aerial cinematography),we present a survey that should assist researchers in positioning and evaluating their works in the context of existing solutions.Results/Conclusions It should also help newcomers and practitioners in related fields quickly gain an overview of the vast literature.In addition to the current research status,we analyze and elaborate on advantages,disadvantages,and potential explorative trends for each application domain.展开更多
基金The participation of X.D.in this research was made possible by the joint PhD grant from the Agence Innovation Défense(AID)and Aix-Marseille UniversityFinancial support for the running costs was provided via a ProxiLearn project grant(ANR-19-ASTR-0009)to F.R.+2 种基金via SpotReturn project grant(ANR-21-ASRO-0001-02)to T.R.and F.R.from the ANR(Astrid Program)X.D.and F.R.were also supported by Aix Marseille University and the CNRS(Life Science,Information Science,and Engineering and Science&technology Institutes)The facilities for the experimental tests has been mainly provided by ROBOTEX 2.0(Grants ROBOTEX ANR-10-EQPX-44-01 and TIRREX ANR-21-ESRE-0015).
文摘Web-like obstacles,such as safety nets,represent a unique hazard for drones,and especially UAVs(Unmanned Aerial Vehicles).Fencing and netting are particularly difficult to distinguish from the background using either computer vision,lidar and sonar.In contrast,animals such as flying insects may detect these web-like obstacles using Optic Flow(OF),and more precisely motion parallax.A netting-avoidance solution was proposed using a OF-based detection method.The netting detection method was based on a signature defined by the shape of the OF magnitude across the visual field.We established that the OF shape depends on the orientation of the netting in relation to the hexarotor’s movement.This paper demonstrates netting detection in real-world experiments,according to any direction flight made by the UAV along the net.The proposed NOWA method(which stands for Netting Optical floW-based distinction Algorithm)separates the OF signatures belonging to these different surfaces-netting or background-whatever their orientations.By extracting the OF signatures of these different surfaces and separating them,the proposed visual method can estimate their relative locations and orientations.In a robotic simulations,the multirotor explores and navigates automatically using this netting detection method,using saccades to avoid obstacles.In the simulations,these saccades are also used to simplify netting detection by orienting itself systematically parallel to these planes,a behavior reminiscent of flying insects.
文摘In this paper, the TAS-I （Thales Alenia Space-Italy） Test Bench for Robotics and Autonomy （TBRA） is presented. It is based on a flexible and modular software architecture （Framework Engine）, in which each functional module （representing the GNC subsystems） implements a key functionality of the GNC （Guidance Navigation and Control）. Modules communicate by means of standardised interfaces designed for exchange of necessary information among the modules composing the entire system. This approach permits the interchange-ability of each subsystem without affecting the overall functionalities of the GNC system. In this paper, the TBRA system, together with the implemented functional modules will be described. Tests results will be reported and future development will be discussed.
基金LHTD(20170003)and the Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ).
文摘Background In recent decades,unmanned aerial vehicles(UAVs)have developed rapidly and been widely applied in many domains,including photography,reconstruction,monitoring,and search and rescue.In such applications,one key issue is path and view planning,which tells UAVs exactly where to fly and how to search.Methods With specific consideration for three popular UAV applications(scene reconstruction,environment exploration,and aerial cinematography),we present a survey that should assist researchers in positioning and evaluating their works in the context of existing solutions.Results/Conclusions It should also help newcomers and practitioners in related fields quickly gain an overview of the vast literature.In addition to the current research status,we analyze and elaborate on advantages,disadvantages,and potential explorative trends for each application domain.
