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Abstract Numerous researchers are committed to finding solutions to the path planning problem of intelligence-based vehicles. How to select the appropriate algorithm for path planning has always been the topic of scholars. To analyze the advantages of existing path planning algorithms, the intelligence-based vehicle path planning algorithms are classified into conventional path planning methods, intelligent path planning methods, and reinforcement learning (RL) path planning methods. The currently popular RL path planning techniques are classified into two categories: model based and model free, which are more suitable for complex unknown environments. Model-based learning contains a policy iterative method and value iterative method. Model-free learning contains a time-difference algorithm, Q-learning algorithm, state-action-reward-state-action (SARSA) algorithm, and Monte Carlo (MC) algorithm.

Abstract Rubber elements present highly nonlinear mechanical properties affected by frequency and amplitude of excitation, prestrain and temperature, etc. Finite element (FE) models and lumped parameter models can be distinguished in the development of constitutive models of rubbers. Based on the concept of overlay model, different kinds of viscoelastic, or frequency-dependent models, and elastoplastic/friction, or amplitude-dependent models, are compared in terms of their modelling approach, parameters identification process and applications. Prestrain-dependent models and temperature-dependent thermo-mechanical models are also reviewed, including some special models which are not based on the concept of the overlay model. Experimental and computational studies of cylindrical bushings subjected to coupled deformation modes are analyzed and discussed.