Category Robotics Classification

Hydraulics are sometimes looked at as an alternative to electric motors.

Some of the primary reasons for this include:

• Linear motion
• Very high torque applications
• Small package for a given torque
• Large number of motors that can share the reservoir/pump can increase volume efficiency
• You can add dampening for shock absorption

However there are also some downsides to using hydraulics including:

• More parts are required (however they can be separated from the robot in some applications)
• Less precise control (unless you use a proportional valve)
• Hydraulic fluid (mess, leaks, mess, and more mess)

Hydraulic systems use an incompressible liquid (as opposed to pneumatics that use a compressible gas) to transfer force from one place to another. Since the hydraulic system will be a closed system (ignore relief valves for now) when you apply a force to one end of the system that force is transferred to another part of that system. By manipulating the volume of fluid in different parts of the system you can change the forces in different parts of the system (Remember Pascal’s Law from high school??).

So here are some of the basic components used (or needed) to develop a hydraulic system.

Pump

The pump is the heart of your hydraulic system. The pump controls the flow and pressure of the hydraulic fluid in your system that is used for moving the actuators.

The size and speed of the pump determines the flow rate and the load at the actuator determines the pressure. For those familiar with electric motors the pressure in the system is like the voltage, and the flow rate is like the electrical current.

Pump Motor

We know what the pump is, but you need a way to “power” the pump so that it can pump the hydraulic fluid. Generally the way you power the pump is by connecting it to an electric motor or gas/diesel engine.

Hydraulic Fluid

Continuing the analogy where the pump is the heart, the hydraulic fluid is the blood of the system. The fluid is what is used to transfer the pressure from the pump to the motor.

Hydraulic Hoses (and fittings to connect things)

These are the arteries and veins of the system that allows for the transfer of hydraulic fluid.

Hydraulic Actuators – Motor/Cylinder

The actuator is generally the reason we are designing this hydraulic system. The motor is essentially the same as the pump; however instead of going from a mechanical input to generating the pressure, the motor converts the pressure into mechanical motion.

Actuators can come in the form of linear motion (referred to as a hydraulic cylinder) or rotary motion motors.

For cylinders, you generally apply a force and the cylinder end extends, and then if you release the force and the cylinder gets pushed back in (think of a car lift). This is the classic and most common use of hydraulics.

For rotary motors there are generally 3 connections on the motor.

• A – Hydraulic fluid input/output line
• B – Hydraulic fluid input/output line
• Drain – Hydraulic fluid output line (generally only on motors, not cylinders)

Depending on the motor you can either only use A as the fluid input and B as the fluid output and the motor only spins in one direction. Or some motors can spin in either direction based on if A or B is used as the input or output of the hydraulic fluid.

The drain line is used so when the system is turned off, the fluid has a way to get out of the motor (to deal with internal leakage and to not blow out seals). In some motors the drain line is connected to one of the A or B lines. Also their are sometimes multiple drain lines so that you can route the hydraulic hoses from different locations.

Note: While the pump and motor are basically the same component. You usually can not switch their role due to how they are designed to handle pressure and the pumps usually not being backdrivable.

There are some actuators that are designed to be leakless and hold the fluid and pressure (using valves) so that the force from the actuator is held even without the pump. For example these are used in things like automobile carrying trucks that need to stack cars for transport.

Reservoir

This is essentially a bucket that holds the fluid. They are usually a little fancier so that they have over pressure relief valves, lids, filters, etc..

The reservoir is also often a place where the hydraulic fluid can cool down if it is getting hot within the system. As the fluid gets hotter it can get thinner which can result in increased wear of your motor and pump.

Filter

Keeps your hydraulic fluid clean before going back to the reservoir. Kind of like a persons kidneys.

Valves (and Solenoids)

Valves are things that open and close to allow the control of fluid. These can be controlled by hand (ie. manual), or more often my some other means.

One common method is to use a solenoid which is a device that when you apply a voltage can be used to open a valve. Some solenoids are latching, which means you quickly apply a voltage and it opens the valves, and then you apply a voltage again (usually switching polarity) to close the valve.

There are many types of valves, I will detail a few below.

Check Valves (One Way Valve)

These are a type of valve that can be inline to allow the flow of hydraulic fluid in only one direction.

Relief Valve

These are a type of valve that automatically opens (And lets fluid out) when the pressure gets to high. This is a safety feature so you don’t damage other components and/or cause an explosion.

Pilot Valve

These are another special class of valve that can use a small pressure to control a much larger pressure valve.

Pressure & Flow-rate Sensors/Gauges 

You need to have sensors (with a gauge or computer output) to measure the pressure and/or flow-rate so you know how the system is operating and if it is operating how you expect it to operate.

Accumulator

The accumulator is essentially just a tank that holds fluid under pressure that has its own pressure source. This is used to help smooth out the pressure and take any sudden loads from the motor by having this pressure reserve. This is almost like how capacitors are used in electrical power circuits.

The pressure source in the accumulator is often a weight, springs, or a gas.

There will often be a check valve to make sure the fluid in the accumulator does not go back to the pump.

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I am not an expert on hydraulic systems. But I hope this quick introduction helps people. Liked it? Take a second to support me on Patreon!

By Rob Matheson

MIT researchers have devised a method for assessing how robust machine-learning models known as neural networks are for various tasks, by detecting when the models make mistakes they shouldn’t.

Convolutional neural networks (CNNs) are designed to process and classify images for computer vision and many other tasks. But slight modifications that are imperceptible to the human eye — say, a few darker pixels within an image — may cause a CNN to produce a drastically different classification. Such modifications are known as “adversarial examples.” Studying the effects of adversarial examples on neural networks can help researchers determine how their models could be vulnerable to unexpected inputs in the real world.

For example, driverless cars can use CNNs to process visual input and produce an appropriate response. If the car approaches a stop sign, it would recognize the sign and stop. But a 2018 paper found that placing a certain black-and-white sticker on the stop sign could, in fact, fool a driverless car’s CNN to misclassify the sign, which could potentially cause it to not stop at all.

However, there has been no way to fully evaluate a large neural network’s resilience to adversarial examples for all test inputs. In a paper they are presenting this week at the International Conference on Learning Representations, the researchers describe a technique that, for any input, either finds an adversarial example or guarantees that all perturbed inputs — that still appear similar to the original — are correctly classified. In doing so, it gives a measurement of the network’s robustness for a particular task.

Similar evaluation techniques do exist but have not been able to scale up to more complex neural networks. Compared to those methods, the researchers’ technique runs three orders of magnitude faster and can scale to more complex CNNs.

The researchers evaluated the robustness of a CNN designed to classify images in the MNIST dataset of handwritten digits, which comprises 60,000 training images and 10,000 test images. The researchers found around 4 percent of test inputs can be perturbed slightly to generate adversarial examples that would lead the model to make an incorrect classification.

“Adversarial examples fool a neural network into making mistakes that a human wouldn’t,” says first author Vincent Tjeng, a graduate student in the Computer Science and Artificial Intelligence Laboratory (CSAIL). “For a given input, we want to determine whether it is possible to introduce small perturbations that would cause a neural network to produce a drastically different output than it usually would. In that way, we can evaluate how robust different neural networks are, finding at least one adversarial example similar to the input or guaranteeing that none exist for that input.”

Joining Tjeng on the paper are CSAIL graduate student Kai Xiao and Russ Tedrake, a CSAIL researcher and a professor in the Department of Electrical Engineering and Computer Science (EECS).

CNNs process images through many computational layers containing units called neurons. For CNNs that classify images, the final layer consists of one neuron for each category. The CNN classifies an image based on the neuron with the highest output value. Consider a CNN designed to classify images into two categories: “cat” or “dog.” If it processes an image of a cat, the value for the “cat” classification neuron should be higher. An adversarial example occurs when a tiny modification to that image causes the “dog” classification neuron’s value to be higher.

The researchers’ technique checks all possible modifications to each pixel of the image. Basically, if the CNN assigns the correct classification (“cat”) to each modified image, no adversarial examples exist for that image.

Behind the technique is a modified version of “mixed-integer programming,” an optimization method where some of the variables are restricted to be integers. Essentially, mixed-integer programming is used to find a maximum of some objective function, given certain constraints on the variables, and can be designed to scale efficiently to evaluating the robustness of complex neural networks.

The researchers set the limits allowing every pixel in each input image to be brightened or darkened by up to some set value. Given the limits, the modified image will still look remarkably similar to the original input image, meaning the CNN shouldn’t be fooled. Mixed-integer programming is used to find the smallest possible modification to the pixels that could potentially cause a misclassification.

The idea is that tweaking the pixels could cause the value of an incorrect classification to rise. If cat image was fed in to the pet-classifying CNN, for instance, the algorithm would keep perturbing the pixels to see if it can raise the value for the neuron corresponding to “dog” to be higher than that for “cat.”

If the algorithm succeeds, it has found at least one adversarial example for the input image. The algorithm can continue tweaking pixels to find the minimum modification that was needed to cause that misclassification. The larger the minimum modification — called the “minimum adversarial distortion” — the more resistant the network is to adversarial examples. If, however, the correct classifying neuron fires for all different combinations of modified pixels, then the algorithm can guarantee that the image has no adversarial example.

“Given one input image, we want to know if we can modify it in a way that it triggers an incorrect classification,” Tjeng says. “If we can’t, then we have a guarantee that we searched across the whole space of allowable modifications, and found that there is no perturbed version of the original image that is misclassified.”

In the end, this generates a percentage for how many input images have at least one adversarial example, and guarantees the remainder don’t have any adversarial examples. In the real world, CNNs have many neurons and will train on massive datasets with dozens of different classifications, so the technique’s scalability is critical, Tjeng says.

“Across different networks designed for different tasks, it’s important for CNNs to be robust against adversarial examples,” he says. “The larger the fraction of test samples where we can prove that no adversarial example exists, the better the network should perform when exposed to perturbed inputs.”

“Provable bounds on robustness are important as almost all [traditional] defense mechanisms could be broken again,” says Matthias Hein, a professor of mathematics and computer science at Saarland University, who was not involved in the study but has tried the technique. “We used the exact verification framework to show that our networks are indeed robust … [and] made it also possible to verify them compared to normal training.”

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Hae Won Park

Dr. Hae Won Park is a Research Scientist in the Personal Robots Group at the MIT Media Lab. She is leading the group’s project to enable long-term personalization of artificially intelligent systems, specifically in areas such as early childhood education, healthcare, eldercare, family interaction, and emotional wellness. Prior to her work at the Media Lab, Dr. Park received her PhD in the Human-Automation Systems (HumAnS) Laboratory at Georgia Tech, where she was advised by Professor Ayanna Howard. Dr. Park is also a co-founder of Zyrobotics, a company that uses technology to assist in childhood education.

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Hopes that the tech industry was on the cusp of rolling personal robots into homes are dimming now that several once-promising consumer robotics companies have shut down.

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