Swedish agriculture has gone through many changes over past years in terms of production, working methods, technology and use of natural resources. To achieve its vision of 2030, Swedish agricultural is rapidly transforming into more and more digitized and automated alternatives and the objective for the research presented in this paper was to identify challenges and opportunities by using automated agricultural vehicles. Data was collected through a survey and review of scientific literature and official reports. The current use and level of automation in Swedish agriculture was studied as well as aspects of productivity, profitability, ergonomics and sustainability by use of regular agricultural vehicles and farming practices. Furthermore, key challenges and opportunities related to automation of agricultural vehicles were further identified. It was found that farmers foresee several areas where automated agricultural vehicles could add value in their production (improved productivity, work environment, environmental impact etc.). However, to utilize these values, there are several challenges identified including technology readiness levels, security, responsibility distribution etc. By overcoming these challenges, it is plausible that the sustainability of agricultural operations with vehicles involved could be significantly improved through automation.

This research was mainly focused on the evaluation of path planning approaches as a prerequisite for the automation of bale collection operations. A comparison between a traditional bale collection path planning approach using traditional vehicles such as tractors, and loaders with an optimized path planning approach using a new autonomous articulated concept vehicle with neighborhood reach capabilities (AVN) was carried out. Furthermore, the effects of carrying capacity on reduction in the working distance of the bale collection operation was also studied. It was concluded that the optimized path planning approach using AVN with increased carrying capacity significantly reduced the working distance for the bale collection operation and can thus improve agricultural sustainability, particularly within forage handling.

Accurate autonomous navigation for off-terrain utility vehicles with no human intervention is an essential requirement to achieve full automation. Within several applications, though higher autonomous navigational accuracy (almost ±2.5 cm) has been achieved in some commercially available vehicles yet requirements for human intervention is still very much required in several situations. This study investigates autonomous navigational accuracy of PurePursuit algorithm for different reference points for a non-steerable-wheels center-steered articulated vehicle. PurePursuit algorithm is preferable choice for path tracking for its simplicity and for vehicles where high speed is not a requirement. Evaluation of PurePursuit algorithm for utility vehicles of type studied in this paper is somewhat less explored area. We have compared the autonomous navigational accuracy of PurePursuit algorithm for different reference points for a set of different path complexities that also includes vehicles kinematic constraints in simulation environment. Average lateral and heading deviations were calculated for a set of different path complexities, and it was found that in general proposed reference point for PurePursuit algorithm for center-steer articulated vehicle shows better lateral and heading autonomous navigational accuracy than the traditional PurePursuit algorithm.

This work presents outdoor test results of autonomous navigation system based on two different reference points of PurePursuit algorithm for a 10-ton articulated research platform. PurePursit is commonly used simplified tracking algorithm based on geometric calculation of desired steering angle by pursuing certain number of points/distance ahead on given path from a fixed reference point of vehicle. Choice of fixed reference point effects the tracking accuracy particularly for articulated/center-steered vehicles. In this experimental work, PurePursuit algorithm with a virtual reference point(PPV) and commonly used front-axle reference point(PPF) is evaluated for heavy duty articulated vehicles in outdoor experiments. Experimental data shows that choice of reference point in PurePursuit algorithm for articulated vehicle has impact on tracking accuracy in terms of crosstrack errors and heading errors. Navigation tests were performed on a flat asphalt surface for paths of sharp complexities i-e a path with continuous curvature (circular path) and a path with sharp turns (zigzag path) with different initial conditions i-e initial position of vehicle. In general, it can be concluded that PurePursuit algorithm with front reference point (PPF) produced fewer crosstrack errors while PurePursuit algorithm with virtual midpoint reference (PPV) produced fewer heading errors.