If you need some sort of distance sensor for your robot, drone, or other project, you have two options: a cheap ultrasonic sensor with limited range, or an expensive laser-based system that’s top of the line. LIDAR-Lite fills that gap by stuffing an entire LIDAR module onto a small board.
In traditional LIDAR systems, a laser is used to measure the time of flight for a light beam between the sensor and an object. The very accurate clock and laser module required for this system means LIDAR modules cost at least a few hundred dollars. LIDAR-Lite gets around these problems by blinking a LED with a ‘signature’ and looking for that signature’s return. This tech is packaged inside a SoC that reduces both the cost and size of a traditional laser-based LIDAR system.
As for the LIDAR-Lite specs, it can sense objects out to 40 meters with
5% 95% accuracy, communicates to any microcontroller over an I2C bus, and is small enough to fit inside any project.
Considering the existing solutions for distance measurement for robots and quadcopters, this sensor will certainly make for some very awesome projects.
Edit: One of the guys behind this posted a link to their spec sheet and a patent in the comments
Getting a device on the internet is great – but what if you want to monitor multiple wireless sensors? The [WickedDevice] crew have been publishing a tutorial series focusing on just that. Their weapon of choice is the Nanode, an Arduino based wireless sensor system we’ve seen a few times in the past. So far the first and second parts have been posted up. Part one starts with an explanation of the Arduino and Nanode platform, and takes us through connecting the Nanode to a wireless temperature sensor. Part two walks through the hardware and code changes to add multiple wireless sensors to the system. Part three will focus on getting the entire network up on the internet, and piping data onto the Xively data hosting site.
This tutorial does begin a bit on the basic side, covering the installation of the Arduino software environment. This may seem a bit simplistic for some of our readers, but we think this type of tutorial is necessary. It helps ‘newbies’ get started down what could otherwise be a difficult path. For more advanced readers, it’s easier to skip past steps you already know than it is to try to hunt down information that isn’t there.
Greenhouse owners might find [David Dorhout]’s latest invention a groundbreaking green revolution! [David]’s Aquarius robot automates the laborious process of precision watering 90,000 square feet of potted plants. Imagine a recliner sized Roomba with a 30 gallon water tank autonomously roaming around your greenhouse performing 24×7 watering chores with absolute perfection. The Aquarius robot can do it all with three easy setups; add lines up and down the aisles on the floor for the robot to follow, set its dial to the size of your pots and maybe add a few soil moisture sensors if you want the perfect amount of water dispensed in each pot. The options include adding soil moisture sensors only between different sized plants letting Aquarius repeat the dispensing level required by the first plant’s moisture sensor for a given series.
After also digging through a pair of forum posts we learned that the bot is controlled by two Parallax propeller chips and has enough autonomous coding to open and close doors, find charging stations, fill its 30 gal water tank when low, and remember exactly where it left off between pit stops. We think dialing in the pot size could easily be eliminated using RFID pot identification tags similar in fashion to the Science Fair Sorting Project. Adjusting for plant and pot size as well as location might easily be automated using a vision system such as the featured Pixy a few weeks back. Finally, here are some featured hardware hacks for soil moisture sensing that could be incorporated into Aquarius to help remotely monitor and attend to just the plants that need attention: [Andy’s] Garden sensors, [Clover’s] Moisture control for a DIY greenhouse, [Ken_S’s] GardenMon(itoring project)
[David Dorhout] has 14 years experience in the agriculture and biotech industry. He has a unique talent applying his mad scientist technology to save the future of mankind as seen with his earlier Prospero robot farmer. You can learn more about Aquarius’s features on Dorhout R&D website or watch the video embedded below.
Continue reading “Fully automated watering robot takes a big leap forward toward greenhouse automation”
When we hear reports of radioactive water leaking into the ocean from the [Fukushima Dai-Ichi] plant in Japan we literally have to keep ourselves from grinding our teeth. Surly the world contains enough brain power to overcome these hazards. Instead of letting it gnaw at him, [Akiba] is directing his skills at one solution that could help with the issue. There are a number of storage tanks on site which hold radioactive water and are prone to leaking. After hearing that they are checked manually each day, with no automated level monitoring, he got to work. Above is the wireless non-contact tank level sensor rig he built to test out his idea.
A couple of things made this a quick project for him. First off, he just happened to have a MaxSonar MB7389 waterproof sonar sensor on hand. Think of this as a really fancy PING sensor that is water tight and can measure distance up to five meters. [Akiba’s] assumption is that the tanks have a hatch at the top into which this sensor would be positioned. The box next to it contains a Freakduino of his own design which includes hardware for wireless communications at 900 MHz. This is the same hardware he used for that wireless toilet monitor.
We really like seeing hacker solutions to environmental problems. A prime example is some of the cleanup hacks we saw around the time of the BP Gulf of Mexico oil spill.
After working on the DARPA Virtual Robotics Challenge this summer, visions of a Heinlenesque robotic actuator filled [Hunter]’s head. His lab had access to something called a Cyberglove that used flexible pots in each of the fingers, but each of these gloves cost the lab $15,000 each.
With a little help from some joystick potentiometers, [Hunter] whipped up a decent approximation of a $15,000 device that measures how much a user’s fingers are bent. The pots are tied into an Arduino and read with analogRead(), while a small Python script interprets the data for whatever application [Hunter] can imagine.
There are a few drawbacks to [Hunter]’s design – it’s not wireless, unlike the $15,000 version, and they certainly don’t look as cool as the real thing. Then again, the DIY version only cost 0.2% as much as the real deal, so we’ll let any apparent problems slide for now.
A while back we featured a magnetic rotary encoder that [LongHairedHacker] designed. The heart of the system is an AS5043 magnetic rotary sensor which runs from $6.5-$11 and has a 10 bits precision. As we wanted to check if his design was really efficient, he made a test bench for it.
For 360 degrees, a 10 bits precision means a ±0.175º accuracy, which is quite impossible to check with conventional measurement equipment. The first approach he thought of was to attach a mirror to the encoders axis and point a laser beam at it. The laser beam would be reflected across the room to a big scale, but the minimum required distance would have been 5 meters (16 feet). So he preferred attaching a motor to the sensor, rotating at a given speed and measuring the sensor output.
In the first part of his write-up, [LongHairedHacker] lays the math which explains the different kinds of errors that should be expected from his setup and sensor. He then proceeds with his test, where an ATMEGA8 based board is used to send the measured position to his computer. It should be noted that [LongHairedHacker] currently uses the time spent between two received measurements on his computer as a time base, but he is planning on time stamping the data on his board in the next future. Nevertheless, he managed to measure an average ±0.179º accuracy with his simple test bench, which is very close to the manufacturer specification.
Here is the link to our original post about his sensor.
There are many different sensors that can be used to detect motion in a given environment. Passive InfraRed (PIR) sensors are the most used today, as they work by detecting moving heat signatures. However, they are less reliable in the hotter days and obviously only work for animals and humans.
Sensors like the one shown in the above picture started to appear on the internet, they use the doppler effect to detect motion. I (limpkin) designed the electronics you need to add in order to get them to work.
Here is a simple explanation of the doppler effect: if you send an RF signal at a given frequency to a moving target, the reflected signal’s frequency will be shifted. It is commonly heard when a vehicle sounding a siren or horn approaches, passes, and recedes from an observer. The received frequency is higher (compared to the emitted frequency) during the approach, it is identical at the instant of passing by, and it is lower during the recession. Continue reading “Making the electronics for a Doppler motion sensor”