scholarly journals Vehicles Platooning in Urban Environments: Integrated Consensus-based Longitudinal Control with Gap Closure Maneuvering and Collision Avoidance Capabilities

2019 ◽  
Author(s):  
Ahmed Khalifa ◽  
Olivier Kermorgant ◽  
Salvador Dominguez ◽  
Philippe Martinet
Keyword(s):  
Collision Avoidance ◽  
Urban Environments ◽  
Longitudinal Control ◽  
Gap Closure

scholarly journals Intent-Estimation- and Motion-Model-Based Collision Avoidance Method for Autonomous Vehicles in Urban Environments

2017 ◽  
Vol 7 (5) ◽  
pp. 457 ◽  
Author(s):  
Rulin Huang ◽  
Huawei Liang ◽  
Pan Zhao ◽  
Biao Yu ◽  
Xinli Geng
Keyword(s):  
Collision Avoidance ◽  
Autonomous Vehicles ◽  
Urban Environments ◽  
Motion Model ◽  
Model Based

scholarly journals FFAU—Framework for Fully Autonomous UAVs

2020 ◽  
Vol 12 (21) ◽  
pp. 3533
Author(s):  
Dário Pedro ◽  
João P. Matos-Carvalho ◽  
Fábio Azevedo ◽  
Ricardo Sacoto-Martins ◽  
Luís Bernardo ◽  
...  
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
New Technology ◽  
Experimental Tests ◽  
Urban Environments ◽  
Novel Approach ◽  
Safety Risks ◽  
Stationary Objects ◽  
Safety And Reliability ◽  
Remote Clients

Unmanned Aerial Vehicles (UAVs), although hardly a new technology, have recently gained a prominent role in many industries being widely used not only among enthusiastic consumers, but also in high demanding professional situations, and will have a massive societal impact over the coming years. However, the operation of UAVs is fraught with serious safety risks, such as collisions with dynamic obstacles (birds, other UAVs, or randomly thrown objects). These collision scenarios are complex to analyze in real-time, sometimes being computationally impossible to solve with existing State of the Art (SoA) algorithms, making the use of UAVs an operational hazard and therefore significantly reducing their commercial applicability in urban environments. In this work, a conceptual framework for both stand-alone and swarm (networked) UAVs is introduced, with a focus on the architectural requirements of the collision avoidance subsystem to achieve acceptable levels of safety and reliability. The SoA principles for collision avoidance against stationary objects are reviewed and a novel approach is described, using deep learning techniques to solve the computational intensive problem of real-time collision avoidance with dynamic objects. The proposed framework includes a web-interface allowing the full control of UAVs as remote clients with a supervisor cloud-based platform. The feasibility of the proposed approach was demonstrated through experimental tests using a UAV, developed from scratch using the proposed framework. Test flight results are presented for an autonomous UAV monitored from multiple countries across the world.

scholarly journals Multi UAV Coverage Path Planning in Urban Environments

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7365
Author(s):  
Javier Muñoz ◽  
Blanca López ◽  
Fernando Quevedo ◽  
Concepción A. Monje ◽  
Santiago Garrido ◽  
...  
Keyword(s):  
Path Planning ◽  
Collision Avoidance ◽  
Urban Environments ◽  
Fast Marching ◽  
Flight Pattern ◽  
Cluttered Environments ◽  
Coverage Path Planning ◽  
Area Of Interest ◽  
Smooth Path ◽  
Multi Uav

Coverage path planning (CPP) is a field of study which objective is to find a path that covers every point of a certain area of interest. Recently, the use of Unmanned Aerial Vehicles (UAVs) has become more proficient in various applications such as surveillance, terrain coverage, mapping, natural disaster tracking, transport, and others. The aim of this paper is to design efficient coverage path planning collision-avoidance capable algorithms for single or multi UAV systems in cluttered urban environments. Two algorithms are developed and explored: one of them plans paths to cover a target zone delimited by a given perimeter with predefined coverage height and bandwidth, using a boustrophedon flight pattern, while the other proposed algorithm follows a set of predefined viewpoints , calculating a smooth path that ensures that the UAVs pass over the objectives. Both algorithms have been developed for a scalable number of UAVs, which fly in a triangular deformable leader-follower formation with the leader at its front. In the case of an even number of UAVs, there is no leader at the front of the formation and a virtual leader is used to plan the paths of the followers. The presented algorithms also have collision avoidance capabilities, powered by the Fast Marching Square algorithm. These algorithms are tested in various simulated urban and cluttered environments, and they prove capable of providing safe and smooth paths for the UAV formation in urban environments.

Autonomous Landing for Indoor Flying Robots Using Optic Flow

2003 ◽  
Author(s):  
William E. Green ◽  
Paul Y. Oh ◽  
Keith Sevcik ◽  
Geoffrey Barrows
Keyword(s):  
Collision Avoidance ◽  
Depth Perception ◽  
Optic Flow ◽  
Urban Environments ◽  
Search And Rescue ◽  
Autonomous Landing ◽  
Aerial Robots ◽  
Aerial Robot ◽  
Detection Search ◽  
Flying Robots

Urban environments are time consuming, labor intensive and possibly dangerous to safe guard. Accomplishing tasks like bomb detection, search-and-rescue and reconnaissance with aerial robots could save resources. This paper describes a prototype called CQAR: Closed Quarter Aerial Robot, which is capable of flying in and around buildings The prototype was analytically designed to fly safely and slowly. An optic flow microsensor for depth perception, which will allow autonomous takeoff and landing and collision avoidance, is also described.

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