As autonomous tractors become more common crop harvesting applications, the need to optimize the global servicing path becomes crucial for maximizing efficiency and crop yield. In recent years, several methods of path generation have been researched, but very few have studied their applications on complex field shapes. In this study, a method of creating the optimal servicing path for simple and complex field shapes is proposed. The proposed algorithm creates subfields for a target land, optimizes the track direction for several subfields individually, merges subfields that result in overall increased efficiency, and finds the minimum non-operating paths to travel from subfield to subfield while selecting the respective optimal subfield starting locations. Additionally, it is required that this process must be done within 3 seconds to meet performance requirements. Results from 3 separate field shapes show that the field traversal efficiency can range from 68.0% to 94.4%, and the coverage ratio can range from 98.8% to 99.9% for several different conditions. In comparison with previous studies using the same field shape, the proposed methods demonstrate an increase of 5.5% in field traversal efficiency.

Excavators are one of several machines that play a vital role in the construction sector. The excavators’ main task is to dig the appropriate shapes in the earth, which may cause severe damage to subsurface infrastructure. There are numerous existing technologies to avoid this. However, they are either too expensive or too time-consuming to utilize. In this research, two affordable magnetometer sensors mounted to the bucket of an autonomous excavator were used to scan the digging area and find metallic pipelines and electricity-carrying cables underground. For this purpose, some theoretical methodologies, as well as AI-based ones, were applied, and their performances were compared. In this study, the researchers employed a combination of derived data, mathematical formulas, and the neural network method to acquire information about underground pipes. The results obtained from this approach demonstrate a close resemblance to actual pipe size and orientation. The implications of this research are significant for the excavation industry, as it provides a higher level of certainty when dealing with underground facilities. These findings can help excavation operations become more cost-effective and time-saving, thereby improving overall efficiency.

This study’s goals are divided into two categories. The first is to design and build an excavator equipped with parallel electrical linear actuators. The second is to generate and test a PSO-based and a PFM-based path for this excavator in order to save energy by reducing energy consumption, improve the digging accuracy by minimizing the deviation between the desired and dug surfaces of the ground, and prevent colliding with subsurface objects. For this purpose, computer vision was employed to improve monitoring and verification. Five types of experiments were carried out in this investigation. The first two and the other three examined the impact of energy conservation in PSO- and PFM-based path generation, respectively. Finally, the results from these experiments were compared to identify and show the effect of optimal path generation.

An autonomous excavator can be a good solution in the construction industry to deal with existing issues such as high labor costs and harsh and hazardous environmental conditions. To increase energy efficiency for autonomous excavators, this study proposes two approaches. First, a new and unique design with parallel arm and bucket actuators is proposed for an electric excavator manipulator. Since the three actuators of the boom, arm, and bucket are in series for the conventional design of excavators, it is difficult to share external loads between them. However, a parallel configuration of the arm and bucket actuators in the proposed new design can facilitate load sharing and overcome higher external loads. By replacing hydraulic actuators with electric linear actuators, this design also reduces energy consumption during idling. Moreover, with low back drivability, the electric linear actuators can handle relatively high external forces without spending energy while not in motion. Secondly, a PSO-based path-generation algorithm was developed for autonomous excavation to minimize energy consumption while avoiding collisions with unwanted obstacles. In the PSO algorithm, it is possible to change the priorities of the elements to the minimum by adjusting the gains in the cost function. Two scenarios—scenarios with and without considering energy saving—were considered to test the performance of the developed algorithm, with the results between the scenarios compared. Simulation results show that the proposed algorithm reduces energy consumption in each digging cycle by 18.51%.