From Tele-Remote Operation to Semi-Automated Wheel-Loader

Dadhich, Siddharth; Bodin, Ulf; Sandin, Fredrik; Andersson, Ulf

doi:10.18178/ijeetc.7.4.178-182

Cited by 11 publications

(7 citation statements)

References 11 publications

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“…In this case, the cameras are usually installed on the machines to collect the surrounding environment information [19]- [21]. Since they typically need more than three cameras to get the information, and the transmission rate of WIFI is limited, they cannot install more cameras to create the depth information resulting in lower productivity even with the very best operators [22]. Considering Virtual reality technology will be adopted with 5G, the difficulty of the remote control will be dramatically reduced.…”

Section: Where Can Construction Sites Be Benefitedmentioning

confidence: 99%

5G Meets Construction Machines: Towards a Smart Working Site

Xiang

et al. 2021

2021 International Conference on Computing and Communications Applications and Technologies (I3CAT)

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The fleet management of mobile working machines with the help of connectivity can increase safety and productivity. Although in our previous study, we proposed a solution to use IEEE 802.11p to achieve the fleet management of construction machines, the shortcoming of WIFI may limit the usage of this technology in some cases. Alternatively, the fifth-generation mobile networks (5G) have shown great potential to solve the problems. Thus, as the world's first academic paper investigating 5G and construction machines' cooperation, we demonstrated the scenarios where 5G can have a significant effect on the construction machines industry. Also, based on the simulation we made in ns − 3, we compared the performance of 4G and 5G for the most relevant construction machines scenarios. Last but not least, we showed the feasibility of remote-control and self-working construction machines with the help of 5G.

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Section: Where Can Construction Sites Be Benefitedmentioning

confidence: 99%

5G Meets Construction Machines: Towards a Smart Working Site

Xiang

et al. 2021

2021 International Conference on Computing and Communications Applications and Technologies (I3CAT)

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“…The algorithm called Proximal Policy Optimization with Covariance Matrix Adaptation was designed to alleviate this problem [15]. The PPO-CMA algorithm is presented in the PPO-CMA study [1].…”

Section: A Reinforcement Learning and Proximal Policy Optimization Wmentioning

confidence: 99%

“…Consequently, many manufacturers of heavy machinery have begun to explore greater automation of machine operations. Tele-remote operation [1] is nowadays possible, which eliminates the aspect of the dangerous environment, but other issues remain. To fully address the challenges associated with heavy earth-moving operations, there is a need for fully automated and operatorindependent machines.…”

Section: Introductionmentioning

confidence: 99%

Automated Excavator Based on Reinforcement Learning and Multibody System Dynamics

et al. 2020

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Fully autonomous earth-moving heavy equipment able to operate without human intervention can be seen as the primary goal of automated earth construction. To achieve this objective requires that the machines have the ability to adapt autonomously to complex and changing environments. Recent developments in automation have focused on the application of different machine learning approaches, of which the use of reinforcement learning algorithms is considered the most promising. The key advantage of reinforcement learning is the ability of the system to learn, adapt and work independently in a dynamic environment. This paper investigates an application of reinforcement learning algorithm for heavy mining machinery automation. To this end, the training associated with reinforcement learning is done using the multibody approach. The procedure used combines a multibody approach and proximal policy optimization with a covariance matrix adaptation learning algorithm to simulate an autonomous excavator. The multibody model includes a representation of the hydraulic system, multiple sensors observing the state of the excavator and deformable ground. The task of loading a hopper with soil taken from a chosen point on the ground is simulated. The excavator is trained to load the hopper effectively within a given time while avoiding collisions with the ground and the hopper. The proposed system demonstrates the desired behavior after short training times.

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“…Despite extensive research in this field, fully automated systems for wheel loaders have never been demonstrated. Remote operation is considered a step towards fully automated equipment, but it has led to a reduction in productivity and fuel efficiency [4].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Reinforcement-Learning-Based Automatic Bucket-Filling for Wheel Loaders

et al. 2021

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Automation of bucket-filling is of crucial significance to the fully automated systems for wheel loaders. Most previous works are based on a physical model, which cannot adapt to the changeable and complicated working environment. Thus, in this paper, a data-driven reinforcement-learning (RL)-based approach is proposed to achieve automatic bucket-filling. An automatic bucket-filling algorithm based on Q-learning is developed to enhance the adaptability of the autonomous scooping system. A nonlinear, non-parametric statistical model is also built to approximate the real working environment using the actual data obtained from tests. The statistical model is used for predicting the state of wheel loaders in the bucket-filling process. Then, the proposed algorithm is trained on the prediction model. Finally, the results of the training confirm that the proposed algorithm has good performance in adaptability, convergence, and fuel consumption in the absence of a physical model. The results also demonstrate the transfer learning capability of the proposed approach. The proposed method can be applied to different machine-pile environments.

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From Tele-Remote Operation to Semi-Automated Wheel-Loader

Cited by 11 publications

References 11 publications

5G Meets Construction Machines: Towards a Smart Working Site

5G Meets Construction Machines: Towards a Smart Working Site

Automated Excavator Based on Reinforcement Learning and Multibody System Dynamics

Data-Driven Reinforcement-Learning-Based Automatic Bucket-Filling for Wheel Loaders

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