Abstract
Recently, various studies related to the development of unmanned vehicles have been conducted around the world. In particular, unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) have been developed and utilized for various purposes. In this study, we developed a method for the path generation of UGVs in a system in which one operator controls many different types of unmanned vehicles. In the driving control system (DCS), it is necessary to process sensor data such as GPS/INS and LiDAR when generating a path by receiving the target waypoint from the ground control station. In addition, the DCS must upload the current location, posture, state, etc., as well as save driving log. Therefore, in order to recognize obstacles in real time and generate a path, a safe path generation algorithm with a short computation time is required. Among the various path generation methods, the potential field algorithm was selected, and the algorithm was modified to reduce the computation time. The computation time before and after modification of the algorithm was obtained and compared through simulation, and the algorithm was verified through application to an actual system by performing an obstacle avoidance experiment and a simultaneous control experiment for two UGVs.
Highlights
Images, while Grocholsky [2] developed a system for local search simultaneously using both unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs)
An environmental recognition and control system for autonomous driving was configured in a UGV driven based on a GPS target point to perform a ground–aerial cooperative mission, and local path planning was performed by applying the potential field algorithm as an optimization-based method
In the developed UGV, GPS/INS processing, LiDAR processing, path planning, and driving log storage had to all be performed by one controller; a path-planning algorithm that could be operated in real time had to be implemented
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The potential field method is broadly applied for real-time collision-free path planning [14]. In this method, an attractive force is generated in the goal direction, and a repulsive force is generated from an obstacle. An environmental recognition and control system for autonomous driving was configured in a UGV driven based on a GPS target point to perform a ground–aerial cooperative mission, and local path planning was performed by applying the potential field algorithm as an optimization-based method. In the developed UGV, GPS/INS processing, LiDAR processing, path planning, and driving log storage had to all be performed by one controller; a path-planning algorithm that could be operated in real time had to be implemented. An obstacle avoidance experiment and an experiment in which one operator simultaneously operates three UAVs and two UGVs were conducted to confirm that the modified potential field algorithm can be used in practice
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