Search

Moving on paths or trails present in natural environments makes autonomous navigation of unmanned ground vehicles (UGV) simpler and safer. In this sense, aerial photographs provide a lot of information of wide areas that can be employed to detect paths for UGV usage. This paper proposes the extraction of paths from a geo-referenced satellite image centered at the current UGV position. Its pixels are individually classified as being part of a path or not using a convolutional neural network (CNN) which has been trained using synthetic data. Then, successive distant waypoints inside the detected paths are generated to achieve a given goal. This processing has been successfully tested on the Andabata mobile robot, which follows the list of waypoints in a reactive way based on a three-dimensional (3D) light detection and ranging (LiDAR) sensor. [ABSTRACT FROM AUTHOR]

Traditional formation control methods are widely used in the field of unmanned ground vehicle formation, but they lack mechanisms with which to effectively cope with complex terrains that occur during movement. In order to better improve the adaptation and coping ability of an unmanned ground vehicles (UGVs) fleet to complex terrains, this paper proposes a formation change influence factor to solve the UGVs formation and formation change problem. First, this paper adopts the leader–follower method with more flexible control to design the formation controller and derives a control law that can make the formation system stable so as to ensure that the fleet maintains the preset formation during movement. After that, this paper combines formation geometry change and dynamic adjustment to build a formation change library. The formation change influence factor is used to drive the fleet to choose the appropriate formation change strategy in the formation change library to ensure the fleet can safely pass the complex terrains. The experimental results show that, compared with the traditional formation method, the UGVs formation and change method using the formation change influence factor can flexibly and efficiently cope with various complex terrains while maintaining stability within the fleet, effectively improving the safety of the UGVs fleet and the possibility of practical application. [ABSTRACT FROM AUTHOR]

Moving on paths or trails present in natural environments makes autonomous navigation of unmanned ground vehicles (UGV) simpler and safer. In this sense, aerial photographs provide a lot of information of wide areas that can be employed to detect paths for UGV usage. This paper proposes the extraction of paths from a geo-referenced satellite image centered at the current UGV position. Its pixels are individually classified as being part of a path or not using a convolutional neural network (CNN) which has been trained using synthetic data. Then, successive distant waypoints inside the detected paths are generated to achieve a given goal. This processing has been successfully tested on the Andabata mobile robot, which follows the list of waypoints in a reactive way based on a three-dimensional (3D) light detection and ranging (LiDAR) sensor.

Traditional formation control methods are widely used in the field of unmanned ground vehicle formation, but they lack mechanisms with which to effectively cope with complex terrains that occur during movement. In order to better improve the adaptation and coping ability of an unmanned ground vehicles (UGVs) fleet to complex terrains, this paper proposes a formation change influence factor to solve the UGVs formation and formation change problem. First, this paper adopts the leader–follower method with more flexible control to design the formation controller and derives a control law that can make the formation system stable so as to ensure that the fleet maintains the preset formation during movement. After that, this paper combines formation geometry change and dynamic adjustment to build a formation change library. The formation change influence factor is used to drive the fleet to choose the appropriate formation change strategy in the formation change library to ensure the fleet can safely pass the complex terrains. The experimental results show that, compared with the traditional formation method, the UGVs formation and change method using the formation change influence factor can flexibly and efficiently cope with various complex terrains while maintaining stability within the fleet, effectively improving the safety of the UGVs fleet and the possibility of practical application.