Autonomous farming uses technologies such as robotics and artificial intelligence to automate agricultural operations, reduce labor requirements, and improve productivity. With a shortage of skilled labor in many parts of the world and increasing demand for food due to population growth, autonomous farming can help farmers increase efficiency, manage larger farms, and improve crop yields and profitability. Furthermore, autonomous farming technologies can lead to improved environmental sustainability by reducing the use of pesticides and fertilizers through targeted and precise application. Mobile robots, such as self-driving tractors, are increasingly being used to automate various agricultural operations such as planting, spraying, and harvesting using advanced algorithms and sensors. The navigation stack, consisting of a set of algorithms and software tools for mobile robot navigation and path planning, is a critical component of autonomous farming. This work presents algorithms for coverage path planning, line-following control, and local navigation used in the navigation stack of a mobile robot, enabling it to operate safely and accurately, thus increasing efficiency and decreasing costs in farming operations.

Coverage path planning (CPP) is an essential component of autonomous farming that enhances the efficiency of agricultural tasks. It provides benefits in terms of cost, time, and quality of coverage. Coverage path planning refers to the process of generating an uninterrupted, collision-free path for a mobile robot to cover a designated area of interest. The aim of this work is to reduce the time and cost of agricultural operations by creating an optimal coverage path using a graph-based representation of the field. The proposed technique is compared to other existing techniques, and it is demonstrated that the proposed technique generates a coverage path with shorter traveled distance (less overlapping) and fewer turns.

The mobile robot should be capable of following the coverage path generated for a field. The coverage path in agricultural applications includes lots of back-and-forth straight-line motions. Therefore, an efficient control approach is required to achieve and follow desired straight lines on the coverage path precisely (to avoid overlaps and skipped areas) and in a time and cost efficient manner. A time-varying model predictive control (MPC) has been designed to determine the appropriate steering angle of a mobile robot for line-following control. To evaluate the performance and efficiency of the designed time-varying MPC technique, it has been compared with a proportional control technique as a common control method for line-following problems. The time-varying MPC proves to outperform the proportional controller in terms of performance and cost.

The coverage path in a field is a global path, which is generated based on a prior knowledge of the field and obstacles. However, if anything changes in this a priori known map of the field, motion plans need to change accordingly, which brings out the notion of local motion planning and navigation around obstacles. In this work, a human-analogous learning technique has been proposed that can learn from a human operator trained in a simulated environment under a learning-by-doing paradigm. A human-in-the-loop simulator, utilized as the training environment for sensor-based motion planning, is developed. A neurofuzzy-based steering algorithm is derived from data collected from a trained human operator. The simulation results are compared to that cited in literature. The proposed algorithm generates superior steering without the need to setting up a cost function and tuning its parameters to generate an efficient local path.