Survey of algorithms for safe navigation of mobile robots in complex environments

Author(s):  
Alexey S. Matveev ◽  
Andrey V. Savkin ◽  
Michael Hoy ◽  
Chao Wang
2003 ◽  
Vol 22 (12) ◽  
pp. 1019-1039 ◽  
Author(s):  
Alessandro Corrêa Victorino ◽  
Patrick Rives ◽  
Jean-Jacques Borrelly

Agriculture ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 954
Author(s):  
Abhijeet Ravankar ◽  
Ankit A. Ravankar ◽  
Arpit Rawankar ◽  
Yohei Hoshino

In recent years, autonomous robots have extensively been used to automate several vineyard tasks. Autonomous navigation is an indispensable component of such field robots. Autonomous and safe navigation has been well studied in indoor environments and many algorithms have been proposed. However, unlike structured indoor environments, vineyards pose special challenges for robot navigation. Particularly, safe robot navigation is crucial to avoid damaging the grapes. In this regard, we propose an algorithm that enables autonomous and safe robot navigation in vineyards. The proposed algorithm relies on data from a Lidar sensor and does not require a GPS. In addition, the proposed algorithm can avoid dynamic obstacles in the vineyard while smoothing the robot’s trajectories. The curvature of the trajectories can be controlled, keeping a safe distance from both the crop and the dynamic obstacles. We have tested the algorithm in both a simulation and with robots in an actual vineyard. The results show that the robot can safely navigate the lanes of the vineyard and smoothly avoid dynamic obstacles such as moving people without abruptly stopping or executing sharp turns. The algorithm performs in real-time and can easily be integrated into robots deployed in vineyards.


2003 ◽  
Vol 22 (12) ◽  
pp. 1005-1118 ◽  
Author(s):  
Alessandro Corrêa Victorino ◽  
Patrick Rives ◽  
Jean-Jacques Borrelly

2019 ◽  
Vol 48 (2) ◽  
pp. 179-194 ◽  
Author(s):  
Ben Beklisi Kwame Ayawli ◽  
Xue Mei ◽  
Moquan Shen ◽  
Albert Yaw Appiah ◽  
Frimpong Kyeremeh

This paper presents optimized rapidly exploring random trees A* (ORRT-A*) method to improve the performance of RRT-A* method to compute safe and optimal path with low time complexity for autonomous mobile robots in partially known complex environments. ORRT-A* method combines morphological dilation, goal-biased RRT, A* and cubic spline algorithms. Goal-biased RRT is modified by introducing additional step-size to speed up the generation of the tree towards the goal after which A* is applied to obtain the shortest path. Morphological dilation technique is used to provide safety for the robots while cubic spline interpolation is used to smoothen the path for easy navigation. Results indicate that ORRT-A* method demonstrates improved path quality compared to goal-biased RRT and RRT-A* methods. ORRT-A* is therefore a promising method in achieving autonomous ground vehicle navigation in unknown environments


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