This paper presents different modeling considerations that are important in simulating visually realistic behavior of underwater cables attached to remotely operated vehicles. The proposed methodology has been tested on highly complex models of aquatic environments created using Unreal Engine 4. Current methods and implementations of cable simulations that are widely used in computer graphics are generally suited only to light density mediums such as air. In this paper, we present modifications to the above model required for simulating neutrally buoyant cables in underwater environments. The simulation results presented in this paper successfully demonstrate different behavioral aspects of flexible variable length underwater cables and their variations with respect to modeling parameters using our proposed method.

This paper presents a framework for simulating visually realistic motion of underwater Remotely Operated Vehicles (ROVs) in highly complex models of aquatic environments. The models include a wide range of objects such as rocks, fish and marine plankton in addition to an ROV tether. A modified cable simulation for the underwater physical conditions has been developed for a tethered ROV. The simulation framework also incorporates models for low visibility conditions and intrinsic camera effects unique to the underwater environment. The visual models were implemented using the Unreal Engine 4 realistic game engine to be part of the presented framework. We developed a generalized method for implementing an ROV dynamics model and this method serves as a highly configurable component inside our framework. In this paper, we explore the unique characteristics of underwater simulation and the specialized models we developed for that environment. We use computer vision algorithms for feature extraction and feature tracking as a probe for comparing experiments done in our simulated environment against real underwater experiments. The experimental results presented in this paper successfully demonstrate the contribution of this realistic simulation framework to the understanding, analysis and development of computer vision and control algorithms to be used in today's ROVs.

This paper presents a generalized framework for the simulation of multiple robots and drones in highly realistic models of natural environments. The proposed simulation architecture uses the Unreal Engine4 for generating both optical and depth sensor outputs from any position and orientation within the environment and provides several key domain specific simulation capabilities. Various components and functionalities of the system have been discussed in detail. The simulation engine also allows users to test and validate a wide range of computer vision algorithms involving different drone configurations under many types of environmental effects such as wind gusts. The paper demonstrates the effectiveness of the system by giving experimental results for a test scenario where one drone tracks the simulated motion of another in a complex natural environment.

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