A novel data fusion algorithm for low-cost localisation and navigation of autonomous vineyard sprayer robots

This work is motivated by the lack of manpower to perform vineyard spraying tasks and the exposure to hazardous pesticides during the spraying. The development of an autonomous system for vineyard spraying would reduce the amount of required labour and redirect it to performing tasks that could increase the farm yield and agricultural profitability and economic survival. Localisation, which is accurately estimating the location of the robot, is a fundamental problem in the field of autonomous mobile robotics. In order to allow basic autonomous navigation of a field robot, a path-planning control law is necessary. This navigation algorithm requires knowledge of the accurate state of the robot at every instance (i.e. position, orientation, linear and angular velocity). Many methods for low cost sensors and state estimation were introduced over the years and each method is based on some assumptions that not always hold in the real field robot case. For example, many state estimation algorithms assume Gaussian noise of the sensors reading. This assumption is not always valid when dealing with GPS, or taking measures in a short time window. Hence it is required to develop an accurate state estimation algorithm that will be based on as many sensors as possible, and will use the advantage of each sensor in an optimal way. Therefore, a new data fusion algorithm is proposed for navigation, that optimally fused the localisation data from various sensors. This paper begins with the development of a kinematic model of the robot that is used for model-based state estimation (filtering). How to filter a noisy raw sensor data and the fusion of data from all sensors (DGPS, IMU and vision) are explored. A novel vineyard sprayer, and its new kinematic structure, is introduced. The methodology for designing a high precision localisation system for sensors data fusion, utilising a likelihood ratio test as a decision-making technique for choosing the most probable state estimation. Each sensor was pre-filtered according to its noise distribution. The localisation algorithm was validated using simulation of the robotic platform and using visual odometry based on real field video data.