No abstract

This paper discusses the estimation of the swing angle and digging depth during the excavation operation. The ability to calculate the excavator's productivity is an essential step toward autonomous excavators. The swing angle and digging depth have significant effects on the excavator's productivity and must be taken into account for the productivity estimation. Two approaches are proposed to estimate these variables. The first method estimates the swing angle using cabin encoder measurements. The local minimum and maximum points are found, and then Otsu's method is exploited to detect the points that are representative of scooping and dumping positions. The second method utilizes the bucket position to estimate the digging depth. The bucket position is calculated using Inertial Measurement Units (IMUs) measurements and the forward kinematics of the excavator. Otsu's method is used to distinguish the local minimum points that are representative of the digging depth during the operation. Moreover, the algorithms are computationally efficient. Finally, the performance of the proposed methods is studied using real measurements. The results show that the methods can effectively estimate the swing angle and digging depth under different working c onditions s uch a s various materials, swing angles, and digging depths.

Heavy-duty mobile machines (HDMMs) are a wide range of machinery used in diverse and critical application areas which are currently facing several issues like skilled labor shortage, poor safety records, and harsh work environments. Consequently, efforts are underway to increase automation in HDMMs for increased productivity and safety, eventually transitioning to operator-less autonomous HDMMs to address skilled labor shortages. However, HDMM are complex machines requiring continuous physical and cognitive inputs from human-operators. Thus, developing autonomous HDMM is a huge challenge, with current research and developments being performed in several independent research domains. Through this study, we use the bounded rationality concept to propose multidisciplinary collaborations for new autonomous HDMMs and apply the transaction cost economics framework to suggest future implications in the HDMM industry. Furthermore, we introduce a conceptual understanding of collaborations in the autonomous HDMM as a unified approach, while highlighting the practical implications and challenges of the complex nature of such multidisciplinary collaborations. The collaborative challenges and potentials are mapped out between the following topics: mechanical systems, AI methods, software systems, sensors, connectivity, simulations and process optimization, business cases, organization theories, and finally, regulatory frameworks.