Intelligent computational techniques for physical object properties discovery, detection, and prediction: A comprehensive survey

Abstract

The exploding usage of physical object properties has greatly facilitated real-time applications such as robotics to perceive exactly as it appears in existence. Changes in the nature and properties of diverse real-time systems are associated with physical properties modification due to environmental factors. These physics-based object properties features attract the researchers’ attention while developing solutions to real-life problems. But, the detection and prediction of physical properties change are very diverse, covering many physics laws and object properties (material, shape, gravitational force, color, state change) which append complexity to these tasks. Instead of well-understood physics laws, elucidating physics laws requires substantial manual modeling with the help of standardized equations and associated factors. To adopt these physical laws to get instinctive and effective outcomes, researchers started applying computational models to learn changing property behavior as a substitute for using handcrafted and equipment-generated variable states. If physical properties detection challenges are not anticipated and required measures are not precluded, the upcoming computational model-driven physical object changing will not be able to serve appropriately. Therefore, this survey paper is drafted to demonstrate comprehensive theoretical and empirical studies of physical object properties detection and prediction. Furthermore, a generic paradigm is proposed to work in this direction along with characterization parameters of numerous physical object properties. A brief summarization of applicable machine learning, deep learning, and metaheuristic approaches is presented. An extensive list of released and openly available datasets for varying objects and parameters rendered for researchers. Additionally, performance measures regarding computational techniques for physical properties discovery and detection for quantitative evaluation of outcomes are also entailed. Finally, a few open research issues that need to be explored in the future are specified.