Wireless Forklift Height Encoder

This project is sponsored by Hyster-Yale Group, who design and manufacture forklifts in the Portland, OR, area. The object of this project is to create a wireless system for accurately measuring the extension of a forklift. This measurement will be used by the operator to easily and accurately return to a given shelf height. It can also be used to implement new control methods, such as entering a numeric height, or having buttons with preset height values. This sensor will essentially take the guesswork out of forklift operation.

Problem Definition
Background Hyster-Yale Group would like to incorporate a forklift height encoder in their products to improve usability as well as to incorporate autonomous operations. The problem with current height sensor designs is either cost, resolution, or durability. In order to meet these requirements, a small, wireless package that mounts on the mast of the forklift must be designed. This requires wireless data transfer as well as energy harvesting for long term, maintenance free operation.

Design Goals and Deliverables Select an appropriate sensing method Wirelessly transmit data to a receiver Harvest energy to allow sensor to run indefinitely Design a package that meets the sizing criteria

Specifications Ideal Measurements Size: 64mm x 90mm x 18mm Sampling Rate: 400Hz Measurement Resolution: 2mm Measurement Height: 7m

Acceptable Measurements Size: 127mm x 127mm x 25mm Sampling Rate: 32Hz Measurement Resolution: 25mm Measurement Height: 4m

Project Learning
Possible Sensor Solutions After researching the design requirements and compiling the engineering specifications, four sensor designs were proposed.  Hall effect sensor</li> <li>RF distance measurement</li> <li>Optical "computer mouse" tracking</li> <li>Mechanical encoded wheel</li> </ul> All of these methods have been evaluated to some extent. The analysis of each method is below. Hall Effect Sensor This method utilizes an analog Hall effect sensor and alternating magnetic tape that would be applied to the mast of the forklift. As the sensor moves up and down the tape, the alternating magnetic field cause the sensor produce a sine wave. Knowing that the positive and negative peaks of this sine wave represent a change in direction of the magnetic field, a simple algorithm was developed to count how many had passed and convert the number to meters. With only one Hall effect sensor, direction of motion cannot be known. This can be rectified by placing another Hall effect sensor at a distance of a quarter lambda away from the first sensor and checking the slope of the sine wave. The Hall effect sensor was tested with great success. It had excellent precision and repeatability. The biggest problem with the system is that permanently securing the magnetic tape inside of the forklift mast could be difficult. It could also get in the way of other hardware on the mast. Below is a picture of one of the trials that we ran. It shows a clean, consistent signal, with acceptable amplitude.

RF Distance Measurement This method was first thought of in order to reduce any mechanical components in the design. A transceiver would be mounted on the forks and another mounted somewhere on the truck, and RF manipulation could triangulate the relative positions. In order to meet the resolution specification, it was found that an antenna array would be necessary to sweep a highly directive beam in order to track the position of the transceiver on the forks. The size and cost of such an antenna array was prohibitive. Another method was to use LiDAR with a line of sight between the transmitter and receiver. Since LiDAR uses optical frequencies, this was ruled out because of scattering. The scattering problems were found during previous experimentation done by Hyster-Yale. Therefore, RF distance measurement has been removed from consideration for this project.

Optical "Computer Mouse" Tracking Although optical distance sensing such as LiDAR had been ruled out from the beginning, a surface tracking device would not suffer from the same scattering problems due to puddles and inconsistent environments. This method of measurement simply outputs a position delta at the sampling rate. These deltas can then be summed in order to get the total distance travelled.

Mechanical Encoded Wheel This is essentially a fallback method, since it is a solved method of distance measurement, but it is very undesirable to have friction based sensing. This is because the wheel can slip over time, and it is also more prone to mechanical failure. This could be somewhat rectified by using a continuous rotary encoder with a reset sensor at the bottom of the forklift stroke. If slip did occur, it would simply recalibrate itself every time the forks were completely lowered. However, the durability problems cannot be entirely overcome.

Current Sensor Experimentation As we have worked through our potential designs, we have decided that the Optical Tracking device should be experimentally pursued first. This data collection has taken place over the course of the last two months. In order to test this method, an optical computer mouse with adjustable DPI was purchased. A component sensor vehicle was designed on Solidworks, so that we may run the mouse over a surface consistently.

A python script was written to harvest the raw data from the mouse. The single mouse was then ran back and forth over a given distance, and the hysteresis error was evaluated. The hysteresis error found during the initial testing was small enough to warrant additional testing and validation. The data is pictured below.

After receiving the above information, we decide to test our vehicles ability to decrease error by offsetting the angles of two mice. We set both sensors in the vehicle and mounted them against the wall for testing.

The python script output data recordings from both sensors. In the data below, the top row of data is from mouse 1. First column is the data from the x axis, second column is data from the y axis, third column is accumulated distance traveled. The second row is data from mouse 2. This mouse recorded massive error, and it has not yet been determined what caused this error.

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