Vision Object-Oriented Augmented Sampling-Based Autonomous Navigation for Micro Aerial Vehicles

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 107
Author(s):  
Xishuang Zhao ◽  
Jingzheng Chong ◽  
Xiaohan Qi ◽  
Zhihua Yang
Keyword(s):  
Autonomous Navigation ◽  
Object Oriented ◽  
Micro Aerial Vehicles ◽  
Cluttered Environments ◽  
Structure Information ◽  
Safety Issues ◽  
Cluttered Environment ◽  
Aerial Vehicles ◽  
Planning Algorithm ◽  
Global Trajectory

Autonomous navigation of micro aerial vehicles in unknown environments not only requires exploring their time-varying surroundings, but also ensuring the complete safety of flights at all times. The current research addresses estimation of the potential exploration value neglect of safety issues, especially in situations with a cluttered environment and no prior knowledge. To address this issue, we propose a vision object-oriented autonomous navigation method for environment exploration, which develops a B-spline function-based local trajectory re-planning algorithm by extracting spatial-structure information and selecting temporary target points. The proposed method is evaluated in a variety of cluttered environments, such as forests, building areas, and mines. The experimental results show that the proposed autonomous navigation system can effectively complete the global trajectory, during which an appropriate safe distance could always be maintained from multiple obstacles in the environment.

scholarly journals Cooperative Vision-Based Object Transportation by Two Humanoid Robots in a Cluttered Environment

2017 ◽  
Vol 14 (03) ◽  
pp. 1750018 ◽  
Author(s):  
Antoine Rioux ◽  
Claudia Esteves ◽  
Jean-Bernard Hayet ◽  
Wael Suleiman
Keyword(s):  
Real Life ◽  
Humanoid Robots ◽  
Whole Body ◽  
Large Object ◽  
Cluttered Environments ◽  
Cluttered Environment ◽  
Planning Algorithm ◽  
Feedback Error ◽  
Object Transportation ◽  
Incremental Construction

Although in recent years, there have been quite a few studies aimed at the navigation of robots in cluttered environments, few of these have addressed the problem of robots navigating while moving a large or heavy object. Such a functionality is especially useful when transporting objects of different shapes and weights without having to modify the robot hardware. In this work, we tackle the problem of making two humanoid robots navigate in a cluttered environment while transporting a very large object that simply could not be moved by a single robot. We present a complete navigation scheme, from the incremental construction of a map of the environment and the computation of collision-free trajectories to the design of the control to execute those trajectories. We present experiments made on real NAO robots, equipped with RGB-D sensors mounted on their heads, moving an object around obstacles. Our experiments show that a significantly large object can be transported without modifying the robot main hardware, and therefore that our scheme enhances the humanoid robots capacities in real-life situations. Our contributions are: (1) a low-dimension multi-robot motion planning algorithm that finds an obstacle-free trajectory, by using the constructed map of the environment as an input, (2) a framework that produces continuous and consistent odometry data, by fusing the visual and the robot odometry information, (3) a synchronization system that uses the projection of the robots based on their hands positions coupled with the visual feedback error computed from a frontal camera, (4) an efficient real-time whole-body control scheme that controls the motions of the closed-loop robot–object–robot system.

A 1920 $\times $ 1080 30-frames/s 2.3 TOPS/W Stereo-Depth Processor for Energy-Efficient Autonomous Navigation of Micro Aerial Vehicles

2018 ◽  
Vol 53 (1) ◽  
pp. 76-90 ◽  
Author(s):  
Ziyun Li ◽  
Qing Dong ◽  
Mehdi Saligane ◽  
Benjamin Kempke ◽  
Luyao Gong ◽  
...  
Keyword(s):  
Energy Efficient ◽  
Autonomous Navigation ◽  
Micro Aerial Vehicles ◽  
Aerial Vehicles

Autonomous Navigation for Micro Aerial Vehicles in Complex GNSS-denied Environments

2015 ◽  
Vol 84 (1-4) ◽  
pp. 199-216 ◽  
Author(s):  
Matthias Nieuwenhuisen ◽  
David Droeschel ◽  
Marius Beul ◽  
Sven Behnke
Keyword(s):  
Autonomous Navigation ◽  
Micro Aerial Vehicles ◽  
Aerial Vehicles

Multi-camera visual SLAM for autonomous navigation of micro aerial vehicles

2017 ◽  
Vol 93 ◽  
pp. 116-134 ◽  
Author(s):  
Shaowu Yang ◽  
Sebastian A. Scherer ◽  
Xiaodong Yi ◽  
Andreas Zell
Keyword(s):  
Autonomous Navigation ◽  
Micro Aerial Vehicles ◽  
Visual Slam ◽  
Aerial Vehicles

scholarly journals Autonomous navigation of micro aerial vehicles using high-rate and low-cost sensors

2017 ◽  
Vol 42 (6) ◽  
pp. 1263-1280 ◽  
Author(s):  
Angel Santamaria-Navarro ◽  
Giuseppe Loianno ◽  
Joan Solà ◽  
Vijay Kumar ◽  
Juan Andrade-Cetto
Keyword(s):  
Autonomous Navigation ◽  
Low Cost ◽  
High Rate ◽  
Micro Aerial Vehicles ◽  
Aerial Vehicles

scholarly journals Visual Homing for Micro Aerial Vehicles Using Scene Familiarity

2018 ◽  
Vol 06 (02) ◽  
pp. 119-130 ◽  
Author(s):  
Gerald J. J. van Dalen ◽  
Kimberly N. McGuire ◽  
Guido C. H. E. de Croon
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Autonomous Navigation ◽  
Indoor Environments ◽  
Micro Aerial Vehicles ◽  
Proof Of Concept ◽  
Starting Position ◽  
Control Actions ◽  
Aerial Vehicles ◽  
Initial Starting

Autonomous navigation is a major challenge in the development of Micro Aerial Vehicles (MAVs). Especially, when an algorithm has to be efficient, insect intelligence can be a source of inspiration. One of the elementary navigation tasks of insects and robots is “homing”, which is the task of returning to an initial starting position. A promising approach uses learned visual familiarity of a route to determine reference headings during homing. In this paper, an existing biological proof-of-concept is transferred to an algorithm for micro drones, using vision-in-the-loop experiments in indoor environments. An artificial neural network determines which control actions to take place.

scholarly journals Adaptive Ultrasound-Based Tractor Localization for Semi-Autonomous Vineyard Operations

Agronomy ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 287
Author(s):  
Matteo Corno ◽  
Sara Furioli ◽  
Paolo Cesana ◽  
Sergio M. Savaresi
Keyword(s):  
Autonomous Navigation ◽  
Incidence Angle ◽  
Autonomous Driving ◽  
Localization Algorithm ◽  
Robust Performance ◽  
Cluttered Environment ◽  
Experimental Campaign ◽  
Commercial Applications ◽  
Agricultural Tractors ◽  
Selection Stage

Autonomous driving is greatly impacting intensive and precise agriculture. Matter-of-factly, the first commercial applications of autonomous driving were in autonomous navigation of agricultural tractors in open fields. As the technology improves, the possibility of using autonomous or semi-autonomous tractors in orchards and vineyards is becoming commercially profitable. These scenarios offer more challenges as the vehicle needs to position itself with respect to a more cluttered environment. This paper presents an adaptive localization system for (semi-) autonomous navigation of agricultural tractors in vineyards that is based on ultrasonic automotive sensors. The system estimates the distance from the left vineyard row and the incidence angle. The paper shows that a single tuning of the localization algorithm does not provide robust performance in all vegetation scenarios. We solve this issue by implementing an Extended Kalman Filter (EKF) and by introducing an adaptive data selection stage that automatically adapts to the vegetation conditions and discards invalid measurements. An extensive experimental campaign validates the main features of the localization algorithm. In particular, we show that the Root Mean Square Error (RMSE) of the distance is 16 cm, while the angular RMSE is 2.6 degrees.

scholarly journals Robust autonomous flight in cluttered environment using a depth sensor

2020 ◽  
Vol 12 ◽  
pp. 175682932092452
Author(s):  
Liang Lu ◽  
Alexander Yunda ◽  
Adrian Carrio ◽  
Pascual Campoy
Keyword(s):  
High Speed ◽  
Point Clouds ◽  
Depth Sensor ◽  
Cluttered Environments ◽  
Industrial Plants ◽  
Cluttered Environment ◽  
Navigation Strategy ◽  
Physical Sensors ◽  
Aerial Vehicle ◽  
Planning Objective

This paper presents a novel collision-free navigation system for the unmanned aerial vehicle based on point clouds that outperform compared to baseline methods, enabling high-speed flights in cluttered environments, such as forests or many indoor industrial plants. The algorithm takes the point cloud information from physical sensors (e.g. lidar, depth camera) and then converts it to an occupied map using Voxblox, which is then used by a rapid-exploring random tree to generate finite path candidates. A modified Covariant Hamiltonian Optimization for Motion Planning objective function is used to select the best candidate and update it. Finally, the best candidate trajectory is generated and sent to a Model Predictive Control controller. The proposed navigation strategy is evaluated in four different simulation environments; the results show that the proposed method has a better success rate and a shorter goal-reaching distance than the baseline method.

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