Design and Simulation of Robot Manipulators Using a Modular Hardware-in-the-loop Platform
Bibliographic record
Abstract
The need for developing high quality systems with short and cost-effective design schedules has created an ongoing demand for efficient prototyping and testing tools In many engineering applications failure of a system can have severe consequences, from loss of hardware and capital to complete mission failure, and can even result in the loss of human life The earliest form of prototyping, physical prototyping, began with the development of the first system, and it refers to fabricating a physical system to evaluate performance and test design alterations. There have been many advances in this field, such as the use of scaled models With the advent of computers a new form of prototyping, termed analytical prototyping, has become a second viable option Computer models are generally inexpensive to develop and can be quickly modified to experiment with various aspects of the system. However, this flexibility often comes at the cost of approximations used to model complex physical phenomena, which in turn lead to inaccuracies in the model and system behaviour. A prototyping tool that has been gaining significant popularity in recent years is hardware-in-the-loop simulation, which can effectively combine the advantages of the two traditional prototyping methods. The underlying concept of hardware-in-the-loop (HIL) simulation is to use physical hardware for system components that are difficult or impossible to model and link them to a computer model that simulates the other aspects of the system. This technique has been successfully applied to development and testing in a wide range of engineering fields, including aerospace This research investigates the application of HIL simulation as a tool for the design and testing of serial-link industrial manipulators, and proposes a generic and modular robotic hardware-in-the-loop simulation (RHILS) architecture. The RHILS architecture was implemented in the simulation of a standard industrial manipulator and evaluated on its ability to simulate the robot and its usefulness as a design tool. The remainder of this section briefly reviews the state-of-the-art in HIL simulation across a broad range of fields, highlighting some of the key benefits and considerations, and then summarizes the current work of other researchers in the specific field of robotic www.intechopen.com Robot Manipulators 348 manipulators. Section 2 presents the details of the RHILS architecture and an analysis of the load emulation mechanism. The hardware setup designed to evaluate the effectiveness and viability of the RHILS architecture is outlined in section 3. This setup was used to simulate a 5-d.o.f. industrial manipulator during a real-world design scenario and the comparison between the RHILS setup and a complete physical prototype is presented in section 4. The conclusions from this research are presented in section 5, discussing the strengths and weaknesses of the RHILS platform implementation and the direction of current research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".