Reactive Collision Avoidance of UAVs with Stereovision Camera Sensors using UKF

Tripathi, Amit Kumar; Raja, Ramsingh G.; Padhi, Radhakant

doi:10.3182/20140313-3-in-3024.00171

Cited by 10 publications

(4 citation statements)

References 5 publications

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“…Because of the limited payload of MAVs, most approaches to obstacle avoidance are camera based (Mori & Scherer, ; Ross et al., ; Schmid, Lutz, Tomic, Mair, & Hirschmüller, ; Magree, Mooney, & Johnson, ; Tripathi, G Raja, & Padhi, ; Flores, Zhou, Lozano, & Castillo, ; Schauwecker & Zell, ; Park & Kim, ). Approaches using monocular cameras to detect obstacles require movement in order to perceive the same surface points from different perspectives for being able to triangulate depth.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Multilayered Mapping and Navigation for Autonomous Micro Aerial Vehicles

Droeschel

Nieuwenhuisen

Beul

et al. 2015

Journal of Field Robotics

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Micro aerial vehicles, such as multirotors, are particularly well suited for the autonomous monitoring, inspection, and surveillance of buildings, e.g., for maintenance or disaster management. Key prerequisites for the fully autonomous operation of micro aerial vehicles are real-time obstacle detection and planning of collision-free trajectories. In this article, we propose a complete system with a multimodal sensor setup for omnidirectional obstacle perception consisting of a three-dimensional (3D) laser scanner, two stereo camera pairs, and ultrasonic distance sensors. Detected obstacles are aggregated in egocentric local multiresolution grid maps. Local maps are efficiently merged in order to simultaneously build global maps of the environment and localize in these. For autonomous navigation, we generate trajectories in a multilayered approach: from mission planning over global and local trajectory planning to reactive obstacle avoidance. We evaluate our approach and the involved components in simulation and with the real autonomous micro aerial vehicle. Finally, we present the results of a complete mission for autonomously mapping a building and its surroundings. C 2015 Wiley Periodicals, Inc.

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Section: Related Workmentioning

confidence: 99%

“…Tripathi et al. () use stereo cameras for reactive collision avoidance. A particularly small and lightweight (only 4 g) stereo system was developed in the DelFly project (De Wagter, Tijmons, Remes, & de Croon, ).…”

Section: Related Workmentioning

confidence: 99%

Multilayered Mapping and Navigation for Autonomous Micro Aerial Vehicles

Droeschel

Nieuwenhuisen

Beul

et al. 2015

Journal of Field Robotics

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show abstract

“…Obstacle avoidance is often neglected, e.g., by flying in a sufficient height when autonomously flying between waypoints. Most approaches to obstacle avoidance for MAVs are camera-based, due to the limited payload [10], [11], [12], [13], [14], [15], [16], [17]. Hence, collision avoidance is restricted to the field of view (FoV) of the cameras.…”

Section: Related Workmentioning

confidence: 99%

Obstacle detection and navigation planning for autonomous micro aerial vehicles

Nieuwenhuisen

Droeschel

Beul

et al. 2014

2014 International Conference on Unmanned Aircraft Systems (ICUAS)

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Abstract-Obstacle detection and real-time planning of collision-free trajectories are key for the fully autonomous operation of micro aerial vehicles in restricted environments.In this paper, we propose a complete system with a multimodal sensor setup for omnidirectional obstacle perception consisting of a 3D laser scanner, two stereo camera pairs, and ultrasonic distance sensors. Detected obstacles are aggregated in egocentric local multiresolution grid maps. We generate trajectories in a multi-layered approach: from mission planning to global and local trajectory planning, to reactive obstacle avoidance.We evaluate our approach in simulation and with the real autonomous micro aerial vehicle.

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“…Operators are constrained by view in the indoor environment or beyond visual range flight and can cause unsafely control that might hurt people, destroy something. Therefore, the collision avoidance system becomes more important (Chee, et al, 2013, Tripathi, et al, 2014. Chong-Ren Xiao (Xiao, 2009) developed collision avoidance algorithm based on fuzzy control and decision tree to make automatic mobile vehicle collision avoidance with merely 9 centimeter per second; Bing-SyunWu (Wu, 2015) integrated fuzzy controller to the quadcopter and verified the possibility of collision avoidance algorithm based on fuzzy control theory.…”

Section: Introductionmentioning

confidence: 99%

Application of Decision Tree on Collision Avoidance System Design and Verification for Quadcopter

Chen

Hsieh

Lai

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The purpose of the research is to build a collision avoidance system with decision tree algorithm used for quadcopters. While the ultrasonic range finder judges the distance is in collision avoidance interval, the access will be replaced from operator to the system to control the altitude of the UAV. According to the former experiences on operating quadcopters, we can obtain the appropriate pitch angle. The UAS implement the following three motions to avoid collisions. Case1: initial slow avoidance stage, Case2: slow avoidance stage and Case3: Rapid avoidance stage. Then the training data of collision avoidance test will be transmitted to the ground station via wireless transmission module to further analysis. The entire decision tree algorithm of collision avoidance system, transmission data, and ground station have been verified in some flight tests. In the flight test, the quadcopter can implement avoidance motion in real-time and move away from obstacles steadily. In the avoidance area, the authority of the collision avoidance system is higher than the operator and implements the avoidance process. The quadcopter can successfully fly away from the obstacles in 1.92 meter per second and the minimum distance between the quadcopter and the obstacle is 1.05 meters.

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Reactive Collision Avoidance of UAVs with Stereovision Camera Sensors using UKF

Cited by 10 publications

References 5 publications

Multilayered Mapping and Navigation for Autonomous Micro Aerial Vehicles

Multilayered Mapping and Navigation for Autonomous Micro Aerial Vehicles

Obstacle detection and navigation planning for autonomous micro aerial vehicles

Application of Decision Tree on Collision Avoidance System Design and Verification for Quadcopter

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