What are some of the benefits and drawbacks of using robotics simulations versus real robots?

There are, of course, pros and cons, let me express my opinion based on my experience in robotics: First, in most of the projects it just saves money. Simulations are cheaper than building real robots for testing. Second, it's better to fail in sim than in real-life. Robots can be tested without any danger of breaking or causing accidents. Last, but not least - simulations help in quickly trying out different ideas and making improvements... and of course, they are a great sales tool for system integrators to better sell their solutions (who does not like moving robots in a cloud platform?)

Simulations are a vital component of system design. They allow teams to design and validate their components before deployment into the field. Here are some items to consider: - Mistakes happen | Simulating allows engineers to optimize robotic routes without damaging any components (arm, EOAT, cell, conveyors, etc.) - Optimization is key | For most robotic applications, there's a constraint on speed vs cost. Simulating various arms makes it possible to pick the right solution and understand trade-offs before incurring costs. - Interaction with other platforms | Most modern simulations allow teams to import & configure external components - mechanical, electrical, and software. Validating their impact is key.

Simulatiksbare highly useful as means of testing whether a concept is even close to working in practice or to find issues and bugs which are harder to find in experiments. Thus, they save you to e and money

Simulations are a great starting point. I use simulations to test my algorithm before implementing it with hardware. This saved money but more importantly, time. The ability to test very specific cases easily and quickly is very important in my opinion.

Simulations are a game-changer in robotics development. They cut costs significantly by eliminating the need for physical hardware, allowing developers to experiment freely without the financial burden. Rapid prototyping becomes a breeze, enabling quick testing of diverse algorithms and accelerating development cycles. Simulations shine in scalability, offering a platform to test under various conditions economically. Safety is a standout advantage; simulations provide a secure testing ground for potentially hazardous scenarios, minimizing risks. The controlled environment allows for extensive algorithm refinement without real-world consequences.

It can be tough to build a realistic simulation, and you never know how realistic it is until you test it in reality. Therefore they have little use other than proof of concept or trying to squash bugs by carefully simulating a specific module.

Simulations can become extremely intricate. It's important to balance requirements, simulation flaws, and the effort required to simulate a system. Here are some pitfalls in simulations: 1. Depending on the system it may require less or more effort to simulate the system with a degree of confidence that is acceptable. Spending additional time will yield diminishing returns. 2. Simulation tools aren't all made equal. It's possible to spend a lot of time integrating non-standard components to get an accurate simulation thus making it too expensive. 3. Engineering change orders are part of most system start-ups. Unforeseen details during the simulation step might render the output irrelevant to the live system.

I agree, there is a big difference between simulation and real world testing. Sometimes hardware works seamlessly in simulation but has communication errors in the real world. I recall an issue with a LIDAR sensor where anything past its range would be read as infinite where as in the simulation it would read its max range. So my code worked perfectly in simulation but not in real world testing.

No simulation on the planet can accurately simulate real life... yet. So it is vital that one understands in what aspects the simulations underperform and which areas they don't.

Simulations will never capture all necessary aspects of reality (often they are really really bad at it). Yet they're invaluable for software development. What is most valuable is a simulator that faithfully mimics the communication interfaces of the real controller. This is useful during development (you don't need to hog an actual robot) but INVALUABLE for test suites that can run thousands of software/hardware combinations in parallel and flag problems before they cause issues in the field.

Using real robots to prototype an application can lead to some advantages: - Custom applications are difficult / time-consuming to replicate in simulation software. Testing the application using a real robot might save the time it will take to build out a simulation that's simply too complex. - Certain critical components, such as EOATs are difficult to simulate and might be easier to test using a live robot. - Using real robots gives a different perspective when it comes to safety for the application. You can't be as "reckless" with the live robot as you might be in the simulation. This typically leads to much careful considerations, changes, software, etc.

From the perspective of a solutions company selling a robot, a live demonstration will give customers a much higher confidence in the proposed solution than a drawing or simulation in most cases.

Real robots bring undeniable strengths to the table. Operating in the actual environment, they provide authentic validation, offering accurate insights into a robot's real-world capabilities. Unlike simulations, real robots use genuine sensors, ensuring the acquisition of precise and unfiltered data essential for robust algorithm development. The realism of hardware-specific challenges is another advantage; real-world testing identifies and resolves issues that simulations might overlook. Although costly, real robots offer unparalleled authenticity and a direct understanding of the complexities inherent in real-world scenarios, making them indispensable for validating and refining robotic systems

Real robots, while invaluable, pose challenges. The primary setback is the significant cost involved, covering initial hardware and ongoing maintenance. This financial hurdle can limit deployment, especially in resource-strapped contexts. Safety concerns are paramount, with real-world testing carrying risks, necessitating precautionary measures that can impede experimentation. Technical challenges abound, including sensor accuracy in unpredictable conditions and potential hardware issues like wear and degradation. Balancing authenticity with cost and safety considerations is vital for navigating the complexities of real robot deployment in robotics development.

Combining simulation testing and real robot testing is essential to optimising the development process of a robotic solution. Deploying a system without testing on the goal platform (real robot) should never be employed as practice from either business or engineering perspective. Regardless of the amount of testing on simulations. Starting the testing process on a real robot without testing at least some components in the simulation first, dramatically increases the risk of failure and additional costs. The ability to test the entire solution with some components being physical and some simulated and changing the simulated to physical and vice versa would be the perfect situation but unfortunately is not possible in most cases.

To seamlessly blend simulations with real robots, embrace Hardware-in-the-Loop (HIL) testing for real-time interaction, connecting the physical robot to simulated environments. Implement transfer learning by initially training algorithms in simulations and fine-tuning with real-world data for adaptability. Establish iterative development cycles with continuous feedback loops between simulations and real-world tests to refine algorithms efficiently. Leverage the Robot Operating System (ROS) for seamless communication, and deploy middleware solutions like ROS 2 or DDS.

Ensure unified simulation environments that closely mimic real-world conditions, using tools like Gazebo or V-REP. Employ data augmentation techniques for simulations to enhance diversity in training data. Design simulations that replicate real-world scenarios accurately, considering environmental complexities and uncertainties. Implement continuous calibration of simulated models to match real-world parameters and integrate safety protocols in both simulated and real environments. By following these technical and practical tips, developers can optimize the integration of simulations and real robots, ensuring robust and adaptable robotic systems.