Abstract
The aim of this study was to design a navigation system composed of a human-controlled leader vehicle and a follower vehicle. The follower vehicle automatically tracks the leader vehicle. With such a system, a human driver can control two vehicles efficiently in agricultural operations. The tracking system was developed for the leader and the follower vehicle, and control of the follower was performed using a camera vision system. A stable and accurate monocular vision-based sensing system was designed, consisting of a camera and rectangular markers. Noise in the data acquisition was reduced by using the least-squares method. A feedback control algorithm was used to allow the follower vehicle to track the trajectory of the leader vehicle. A proportional–integral–derivative (PID) controller was introduced to maintain the required distance between the leader and the follower vehicle. Field experiments were conducted to evaluate the sensing and tracking performances of the leader-follower system while the leader vehicle was driven at an average speed of 0.3 m/s. In the case of linear trajectory tracking, the RMS errors were 6.5 cm, 8.9 cm and 16.4 cm for straight, turning and zigzag paths, respectively. Again, for parallel trajectory tracking, the root mean square (RMS) errors were found to be 7.1 cm, 14.6 cm and 14.0 cm for straight, turning and zigzag paths, respectively. The navigation performances indicated that the autonomous follower vehicle was able to follow the leader vehicle, and the tracking accuracy was found to be satisfactory. Therefore, the developed leader-follower system can be implemented for the harvesting of grains, using a combine as the leader and an unloader as the autonomous follower vehicle.
Highlights
Multiple autonomous vehicles can improve the efficiency of agricultural operations by performing labor-intensive tasks such as transporting, plowing, sowing, fertilizing, spraying, and harvesting [1,2].The simultaneous control of multiple robotic vehicles has received attention from several researchers.For example, multiple moss-harvesting robotic tractors were commanded and monitored by a human driver, who functioned as the leader [3]
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Summary
Multiple autonomous vehicles can improve the efficiency of agricultural operations by performing labor-intensive tasks such as transporting, plowing, sowing, fertilizing, spraying, and harvesting [1,2]. Multiple moss-harvesting robotic tractors were commanded and monitored by a human driver, who functioned as the leader [3]. To enable navigation under complex road conditions, an autonomous follower tractor could change formation with the human-driven leader tractor to avoid obstacles based on commands from the leader [4]. The FOLLOW and GOTO algorithms were developed to control multiple vehicles in a flexible way, both in formation and independently [5]. When considering the farming task style, a common operational method of multiple autonomous vehicles should be effective when an autonomous or a human-driven leader vehicle can lead one or more follower vehicles. While driving along a narrow road with obstacles, followers can adopt an in-line formation with the leader for safety, whereas while
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