Development of an Earthmoving Machinery Autonomous Excavator Development Platform

HeikkilÃ, Rauno; Makkonen, Teemu; Nishanen, Ilpo; Immonen, Matti; Hiltunen, Mikko; Kolli, Tanja; Tyni, Pekka

doi:10.22260/isarc2019/0134

Cited by 7 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…controlling motion of the bucket. Unlike traditional excavators, autonomous excavators now have become perceptive, as they are equipped with cutting edge sensors, cameras, a global positioning system (GPS), lidar, radar, and laser beams mostly [3,6]. Such equipment enhances the autonomy of the excavators, as they can collect raw data, and analyze the data using machine learning to develop an understanding of the designated environment.…”

Section: Methods For Autonomous Excavatorsmentioning

confidence: 99%

“…The automation level for excavators have been categorized into various types, the most common are, i) humancontrolled machines, ii) remote-controlled machines, iii) semi-autonomous machines, iv) fully-autonomous machines [3,2]. .…”

Section: Introductionmentioning

confidence: 99%

“…The automation and autonomy traits in excavators can however be increased, considering the ongoing challenges in construction engineering and earth moving machines area. Apart from the heterogeneous and circumstance-dependent characteristics of the terrain, ensuring the safety and efficiency of these machines is a great challenge toward fully autonomous excavators [3].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Road map for implementing AI-driven Oulu Smart excavator

Mehmood¹,

Hiltunen²,

Makkonen³

et al. 2021

Proceedings of the 38th International Symposium on Automation and Robotics in Construction (ISARC)

Self Cite

View full text Add to dashboard Cite

Machine control systems are advancing side by side with the adoption of 5G and growing trends of the internet of things (IoT) has made autonomous excavators more ubiquitous. The autonomous excavators have gained significant interest in the earthworks area, due to their enhanced productivity for long hours, safety, space exploration, mining and construction work. However, A great amount of effort is required to address many existing challenges such as adaptive movement and control, task planning (digging, moving debris, etc.), collaborative work with other machines and humans. In this study, we review the state of the art and provide artificial intelligence (AI-) driven road map for implementing a complete autonomous framework for the earth-moving machine to our test platform 'Smart Excavator'. Furthermore, the challenges and required effort to implement the framework are also discussed in comparison with existing literature.

show abstract

Section: Methods For Autonomous Excavatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Road map for implementing AI-driven Oulu Smart excavator

Mehmood¹,

Hiltunen²,

Makkonen³

et al. 2021

Proceedings of the 38th International Symposium on Automation and Robotics in Construction (ISARC)

Self Cite

View full text Add to dashboard Cite

show abstract

“…The smart excavator used in this study was developed by Oulu University's Civil Engineering Research Unit from a standard Bobcat E85 Compact excavator. It is fully automated and can be operated with four different control methods (Immonen, 2014and Heikkilä et al, 2019and Mehmood et al, 2021. The first is typical manual operation mode, whereby a pilot operates the controls of the excavator from inside the cockpit.…”

Section: Smart Excavator Specificationsmentioning

confidence: 99%

Adaptive Utilization of Pressure Sensor Data in Excavation

Juola,

Heikkilä,

Immonen

et al. 2024

Journal of Earth Sciences and Geotechnical Engineering

View full text Add to dashboard Cite

The assessment of soil parameters in construction holds significant importance for refining building information modeling (BIM). Our study aimed to investigate the adaptive utilization of pressure sensor data as a dynamic and computationally efficient tool for this purpose. The results reveal a significant correlation between the pressure sensor readings of the hydraulic cylinder in the excavator bucket and the total load during static-dynamic penetration tests conducted in both homogeneous and heterogeneous soil. This correlation holds true across a 100% range of torque, with values recorded at 0.60 and 0.93, respectively. A key strength of this methodology lies in that it enables near real-time detection of verified boundary levels. This feature streamlines the adoption and development of BIM-based excavation methods that seamlessly align with current practical conditions. Keywords: Soil boundary, detection, hydraulic pressure, static-dynamic penetration test, correlation, model-based design, ground investigation.

show abstract

“…While for automation of passenger cars, the SAEJ3016 taxonomy of automation levels (Taxonomy, 2021) has become a de facto standard (despite other existing standards (Hopkins and Schwanen, 2021)], automation taxonomies for heavy machinery or load handling vehicles are mostly specific to application fields, such as agriculture (Benos et al, 2020), constructions sites (Lee et al, 2022), or mining (Rogers et al, 2019). Only recently, Machado et al (Machado et al, 2021a) proposed an approach that makes reference to several preliminary models (Heath, 2018;Heikkilä et al, 2019;Krug et al, 2019), which is essentially constituted of a 2-dimensional matrix, where both for driving and for handling (or "manipulation") the six levels of the SAEJ3016 are applied.…”

Section: Level Of Automationmentioning

confidence: 99%

A design space for automated material handling vehicles

Mirnig,

Fröhlich,

Zafari

et al. 2023

Front. Robot. AI

View full text Add to dashboard Cite

Material Handling Vehicles (loaders, excavators, forklifts, harvesters, etc.) have seen a strong increase in automation efforts in recent years. The contexts such vehicles operate in are frequently complex and due to the often very specific nature of industrial material handling scenarios, know-how is fragmented and literature is not as numerous as, for example, for passenger vehicle automation. In this paper, we present a contextual design space for automated material handling vehicles (AMHV), that is intended to inform context analysis and design activities across a wide spectrum of material handling use cases. It was developed on the basis of existing context and design spaces for vehicle and machine automation and extended via expert knowledge. The design space consists of separate context and interaction subspaces, that separately capture the situation and each individual point of interaction, respectively. Implications, opportunities, and limitations for the investigation and design of AMHV are discussed.

show abstract

Development of an Earthmoving Machinery Autonomous Excavator Development Platform

Cited by 7 publications

References 0 publications

Road map for implementing AI-driven Oulu Smart excavator

Road map for implementing AI-driven Oulu Smart excavator

Adaptive Utilization of Pressure Sensor Data in Excavation

A design space for automated material handling vehicles

Contact Info

Product

Resources

About