This paper introduces a new computational multibody system framework for developing accurate tire models using the finite element absolute nodal coordinate formulation (ANCF). Absolute nodal coordinate formulation finite elements are used to create the geometry and perform the finite element/multibody system analysis of the tires. The computational procedure used in this study allows for modeling composite tires and for using a continuum-based air pressure and contact tire force models. The absolute nodal coordinate formulation tire mesh, which allows for high spinning speed, has a constant inertia matrix and zero Coriolis and centrifugal forces. The concept of the absolute nodal coordinate formulation reference node, introduced recently, is used to develop linear connectivity conditions between the tire tread and rim, thereby allowing for imposing these linear conditions at a preprocessing stage. Using this approach, the dependent variables are eliminated at a preprocessing stage before the start of the simulation. The reference node, which is not associated with a particular finite element, is used to define the inertia of the rigid rims. The inertia coefficients associated with the rim reference nodes are first developed in terms of the absolute nodal coordinate formulation position and gradient coordinates. The rigidity of each rim is enforced during the dynamic analysis using six nonlinear algebraic constraint equations that are combined with the dynamic differential equations of motion using the technique of Lagrange multipliers. It is shown in this investigation that the concept of the absolute nodal coordinate formulation reference node can be used to develop a complete vehicle model using one absolute nodal coordinate formulation mesh in which the redundant variables are systematically eliminated at a preprocessing stage, and consequently, the number of differential and algebraic equations that need to be solved is significantly reduced. The use of the new approach proposed in this paper is demonstrated using a vehicle model described by one absolute nodal coordinate formulation mesh.

Many modern applications of the flexible multibody systems require formulations that can effectively solve problems that include large displacements and deformations having the ability to model nonlinear materials. One method that allows dealing with such systems is continuum-based absolute nodal coordinate formulation (ANCF). The objective of this study is to formulate an efficient method of modeling nonlinear nearly incompressible materials with polynomial Mooney-Rivlin models and volumetric energy penalty function in the framework of the ANCF. The main part of this paper is dedicated to the examination of several ANCF fully parameterized beam elements under incompressible regime. Moreover, two volumetric suppression methods, originating in the finite element analysis, are proposed: a well-known selective reduced integration and F-bar projection. It is also presented that the use of these methods is crucial for performing reliable analysis of models with bending-dominated loads when lower-order elements are employed. The results of the simulations carried on with considered elements and proposed methods are compared with the results obtained from commercial finite element package and existing ANCF implementation. The results

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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.

Modeling and analysis of complex dynamical systems can be effectively performed using multibody system (MBS) simulation software. Many modern MBS packages are able to efficiently and reliably handle rigid and flexible bodies, often offering a wide choice of different formulations. Despite many advances in modeling of flexible systems, the most widely used formulation remains the well-established floating frame of reference formulation (FFRF). Although FFRF usually allows inclusion of only small elastic deformations, this assumption is adequate for many industrial applications. In addition, FFRF is computationally efficient if implemented with appropriate model order reduction techniques and effective handling of system inertia terms by utilization of so-called inertia shape integrals. However, derivation of the system of equations of motion for FFRF bodies is a complex and often error-prone task. The main goal of this paper is to provide a reliable, detailed, universal and