Automated Excavator Based on Reinforcement Learning and Multibody System Dynamics
| Autor: | Ilya Kurinov, Grzegorz Orzechowski, Perttu Hamalainen, Aki Mikkola |
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| Přispěvatelé: | LUT University, Professorship Hämäläinen Perttu, Department of Computer Science, Aalto-yliopisto, Aalto University |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
real-time simulation
reinforcement learning multibody system dynamics General Computer Science Computer science Autonomous agents 0211 other engineering and technologies discrete event dynamic automation systems 02 engineering and technology 021105 building & construction 0202 electrical engineering electronic engineering information engineering Reinforcement learning General Materials Science Electrical and Electronic Engineering Hydraulic machinery CMA-ES Representation (mathematics) General Engineering Control engineering Multibody system Reinforcement learning Adaptation models Automation Computational modeling Training Task analysis Load modeling Excavator Task analysis learning and adaptive systems 020201 artificial intelligence & image processing lcsh:Electrical engineering. Electronics. Nuclear engineering lcsh:TK1-9971 |
| Zdroj: | IEEE Access IEEE Access, Vol 8, Pp 213998-214006 (2020) |
| Popis: | openaire: EC/H2020/845600/EU//RealFlex 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 article 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 multibodymodel 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. |
| Databáze: | OpenAIRE |
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