A collaborative robot is essentially an industrial robot with additional safety capabilities. These safety features include:
- Safety-rated monitored stop (zero speed limiting)
- Speed and separation monitoring (limiting)
- Power and force limiting (PFL)
The collaborative features listed above are meant to make it easier to interact with or use the robot by allowing for alternate safety solutions to help protect the operator instead of using the time-tested, but restrictive method of hard guarding. An example would be a large palletizing robot system utilizing ESPE (Electro Sensitive Protective Devices), in conjunction with speed and separation monitoring to reduce the robot speed when an operator presence is detected, and to limit the robot range of motion (in lieu of hard guarding around the robot). Another example would be a machine load/unload application where an industrial robot is using the safety-rated monitored stop feature to allow an operator to interact with the robot in the collaborative workspace. The PFL feature allows an operator to work alongside a moving robot with reduced risk of injury in the case of an operator-robot collision. This is done by limiting the power or force transmitted to the operator during either a transient (dynamic) event or in a quasi-static (clamping/crushing) situation.
Major industrial robot brands have supported items one and two of the above capabilities for over a decade. Hand-guiding has also been implemented on industrial robots as an application- level add-on. Power and force limiting have not been supported until recently on industrial robots. From the customer perspective, a collaborative robot can limit its power and force, thereby limiting the injury it can cause an operator who is working in close proximity to the robot.
Additionally, customers perceive these robots to be smaller, slower, lighter, and easier to use (all factors geared toward easy and safe operator interaction). The PFL feature is the one that is actually being used to target new applications that involve the use of robots in unstructured environments driven by flexible plant layouts and frequent human intervention. This intervention may be continuous where a human works alongside the robot, or intermittent where a human may intervene in the robot workspace to recover from errors or take over some tasks, or as a result of operator error.
Human assist is an application area where customers can augment a robot’s precision and load-carrying capacity with human judgement. The hand-guiding feature of a collaborative robot will be utilized for such applications. Hand-guiding will also be useful in easy recovery from fault conditions as opposed to what’s common today, where a well-trained operator has to jog a robot in three-dimensional space from its faulted position to a safe position using a teach pendant.
In practice, a collaborative robot is only different from a traditional industrial robot because it is power- and force-limiting. For example, these robots are not necessarily any smarter in terms of dealing with unstructured environments or recovering from faults that may occur during complex assembly processes. However, customers do expect collaborative robots to have easy-to-use interfaces that are suited for applications that involve frequent operator interaction, and for a workforce that is not familiar with industrial robots. As such, the intelligent aspects of these robots are mostly in their operator interface, which makes complex robot programming easily accessible to a minimally trained operator. This ease of use allows customers to self-deploy these robots and reprogram them with ease. This reduces overall cost, thereby justifying the return on investment for a low-volume robot user.
There are still significant safety concerns when robots and humans work side-by-side, and these concerns involve the overall system/solution and not just the robot arm. To date, highly trained engineers who are exploring automation possibilities have been the ones who have mostly deployed collaborative robots. Some of these deployments have involved overall solution-risk assessment. As these robots proliferate, their deployment will move from the first adopters to more process-driven plant personnel. As this happens, the safety standards will increasingly require systematic risk assessment before a solution is deployed.
The table on page 1a lists the tasks a typical robot programmer must complete for a given application. This analysis covers three scenarios:
- A traditional industrial robot deployed in a high-production-rate palletizing application without any palletizing application software.
- A collaborative robot deployed in an isolated handling application that involves safe human intervention using the PFL capability of the robot.
- An industrial robot deployed in the same application as 2), though in this case, the ingress into the robot’s workspace will be monitored using a laser scanner in order to automatically reduce the robot’s speed on operator entry.
Most industrial robot applications involve integration with quite a few peripherals as the robot is part of a larger overall automation process. This leads to difficulty in initial machine building as well as complex startup, shutdown, and error recovery routines. Additionally, industrial robot applications are driven generally by cycle time, and programmers often devote a large portion of their development to optimization. Conversely, most collaborative robot applications are isolated applications that are not driven by cycle time. Teaching points and programming robot logic are considered smaller “pain points” when compared to other tasks. Using an industrial robot in a collaborative application will involve additional sensors (e.g., laser scanner) that detect the presence of a human. The robot can then limit its workspace and speed based on this presence. In the case of a collaborative robot, it can rely on its power and force limitation to mitigate risk of injury to the operator. Also, the hand-guiding supported by the collaborative robot will simplify fault recovery and enable lesser trained operators.
Industrial robots could do the following to reduce the complexity associated with programming: provide application software (such as MotoSight™ for vision); offer guidance to the operator through the startup and shutdown implementation processes; offer an organized way to define error states that can easily be linked to error recovery routines; and support cycle-time optimization by continuously providing cycle-time information and recommending changes to teach points and placement of various objects in the robot cell.
In addition to robot programming, significant time is devoted to the development of the end-effector tool. Although engineers have successfully standardized robot arms for different applications, there is still very little standardization of end-effector tooling.
Supporting Return on Investment
Robot integration costs depend on labor (e.g., programming, design, wiring, risk assessment, testing, tooling, etc.) and peripherals (e.g., tooling, safety guarding, conveyance, IO, PLC, HMI, etc.). Labor cost is reduced by providing customers with easy-to-use software that reduces programming time and eliminates the need for hardwiring (digital vs. analog). Easy-to-use programming can again be achieved in multiple ways. For example, many (material handling) robots get deployed with PLCs, and the number of PLC programmers is significantly higher than robot programmers. As such, a PLC robot controller that is central to all peripheral integration reduces the number of software environments required to create the solution, eliminates redundant hardware between the robot controller and PLC (such as IO, fieldbus interfaces, etc.), reduces factory acceptance costs, and reduces troubleshooting time and costs.
Another way to achieve ease of use is by creating high-level application software modules that reduce the amount of programming required. These are often used in palletizing applications (to automatically create pallet patterns) or high-speed picking applications (to automatically schedule workpieces across multiple robots). Another often overlooked means of achieving ease of use is the development of user interfaces that are better aligned with current consumer technology and design principles.
Peripheral cost is reduced by adding features to robot arms and their software that eliminate the need for external hardware. For example, collaborative features may reduce the need for hard-guarding (e.g., fencing, light curtains, etc.). Also, software-based safety systems can be reprogrammed to expand application scope; for instance, a handling application that automatically reconfigures safety zones that the robot can enter and operate in. Another example is when networked safety is used to eliminate hard-wired drops for safety IO and emergency stops.
Almost all industries that involve humans doing non-process applications such as handling (which includes pick and place) and assembly will benefit from collaborative robots. The applications that benefit the most will be able to tolerate a slightly more variable and slower production rate, and will have tooling and peripherals that are conducive to human safety. Let the collaboration begin.
This article was written by Chetan Kapoor, senior director of technology innovation for Yaskawa Innovation, Inc., Austin, TX. For more information, contact Chetan at