The power of simulation has revolutionized the robotic industry, supporting the design, analysis,
and results in different research and development areas. Simulation is the process of designing
a virtual model of an actual or theoretical physical system, describing its environment, and
analyzing its output while varying the designated parameters. The capability of creating a digital
twin and reconstructing its surroundings without the need for a physical prototype allows
companies to save time and money on their concept models. Now, institutions no longer need to
manufacture expensive, time-consuming iterative prototypes, but instead, they can generate
digital data representing their desired system. Simulation allows us to study the structure,
characteristics, and function of a robotic system at different levels of detail, each having different
requirements.
Robotic simulation provides proof of concept and design, ensuring that flaws do not get built into
automated systems. They can be used to analyze kinematics and dynamics of robotic
manipulators, construct different control algorithms, design mechanical structures, and organize
production lines. The simulation advancements allow physically accurate digital copies to be
built and operate in real-time ray and path tracing for true-to-life visualization without
compromising accuracy.
Robotic Simulations Across Industries
Robotic simulation has allowed the production of more customizable, compatible, accurate, and
automated products. The automobile industry can leverage these attributes through
multi-physics packages that support ground vehicle modeling, simulation, and visualization. One
such library is Chrono::Vehicle developed by members of the University of Wisconsin-Madison
and the University of Parma and funded by the U.S. Army. This software package is designed in
a modular manner, using a template-based approach to allow rapid prototyping of existing and
new vehicle configurations. It also has large-scale vehicle-terrain-environment ability for
multi-physics and multi-scale simulation. Although vehicles can be complex with intricate
connectivity and precise design configurations, their systems have relatively standard
topologies. These predefined frameworks allow the developers to design modeling tools based
on a modular approach. The modules represent the vehicle’s subsystems such as suspension,
steering, and driveline.The template defines the essential modeling elements such as bodies,
joints, and force elements. The template parameters are the hardpoints, joint directions, inertial
properties, and contact material properties.
Several studies can be performed with this modular approach such as standard mobility testing on rigid flat terrain, double lane change with a path-follower driver system, and constant-speed controller to find the maximum speed at which the vehicle can safely perform the maneuver. The simulations also include a step-climbing validation test for determining the maximum obstacle height that a tracked vehicle can accomplish from rest.
Chrono::Vehicle can simulate fording maneuvers and sloshing of liquids in
vehicle-mounted tanks. Autonomous vehicles can also be simulated such as a convoy of cars
equipped with virtual LiDAR, GPS, and IMU sensors that allow the fleet to follow the car ahead.
In 2000, the US Food and Drug Administration (FDA) approved surgical robotic devices for
human surgery. Because robotic surgery requires a different set of surgical skills from
conventional surgery, robotic surgery simulators allow surgeons to be properly trained to safely
adopt this innovative technology. Robotic simulators that can provide automated, objective
performance assessments are useful for training surgeons and provides a safe environment for
learning outside of the operating room. Such a device was developed by 3D Systems, formerly
Simbionix, called the RobotiX mentor which is a stand-alone simulator of the da Vinci robot, a
surgical system that allows surgeons to perform complex minimally invasive surgeries. Its
software replicates the functions of the robotic arms and the surgical space. It offers complete
surgeries and 53 procedure-specific exercises in a fully simulated anatomical environment with
tissue behavior. This gives students a reproducible environment while providing complications
and emergent situations similar to those that might occur during a real operation.
Overall, robotic simulation is viewed as a modality that allows physicians to perform increasingly
complex minimally invasive procedures while enhancing patient safety. All in all, robotic simulation makes it possible to design robots, rapidly test algorithms, perform regression testing, and train systems using realistic scenarios. Data collection is relatively inexpensive, simulation can procedurally generate scenes, and state information is trivially available. Reduced learning on the real robot is also highly desirable as simulations are frequently faster than real-time while safer for both the robot and its environment.
