Our CTO (Luke Cole) previously worked for Hemisphere GPS (orginally called BEELINE, and now bought out by AgJunction) as a "Robotics Engineer" implementing auto-guidance solutions for various quadbikes and agriculture tractors that was used by 100's of vehicles around the world.
For 10 years, starting as a teenager in 1998 - Luke Cole has also worked for leading research institutes and companies such as NICTA (now called CSIRO Data61), CSIRO, Seeing Machines and ANU Robotics System Lab (lead by Alex Zelinsky, who received a rare prestigious AO award in 2017 and was Defence Scientist of Australia from 2012 for 6 years). Luke's worked included various autonomous mobile robot projects, involving computer vision, and even a self-driving car early 2000's. Back then OpenCV and ROS didn't exist, so we did a "roll-your-own" called VisLib and DROS comprised of 364,578 lines of code.
Lance Cole has also worked at NICTA and has a background of various hardware development, such as working for a contract company to the US millary (EOS), building the Common Remotely Operated Weapon Station (CROWS).
We have a long-standing robotics experience - our engineers offering Canberra Robotics Prototyping, robot development and robotics custom software services have something like a combined 50 years worth of experience in the robotics field, from teleoperated and semi-autonomous mobile robotic applications, to custom software and/or custom hardware for general automation solutions, signal processing and control systems. Our knowledge base started in NSW and ACT, but now we primarily service East Coast Queensland.
We have developed autonomous mobile robots for air, underwater and ground. Our professional experience started with developing various control and sensor systems for small underwater vehicles in the late 1990's. We where fortunate enough to have been involved in one of the first self-driving car R&D projects back in early 2000's using a 4WD (to support computers for the large processing requirements). For an overseas client we developed a low profile (70mm high) semi-autonomous mobile robot platform for manikin/dummy mounting to simulate people moving (for vehicle crash safety and collision development by German R&D car manufactures). We have been fortunate enough to have been invited to the bulk of the German R&D car manufactures where they develop and test self-driving and driver assist development systems. We have developed various solutions for 2cm accuracy precision steering-guidance solutions for various types of Agriculture tractors (via the CAN bus and ad-hoc methods), which are still used by 1000's of tractors all over the world. We have retro fitted Quadbikes to allow semi-autonomous control via GPS way points. We have custom developed various indoor mobile robotics for indoor localistion and SLAM R&D purposes.
Robot navigation is the task where an autonomous robot moves safely from one location to another. This involves three primary questions:
For robotic localisation and obstacle avoidance we use sensors to solve the problem. To move along the planned path, we use control systems.
We have a deep understanding of signal processing and sensors of various types. We appreciate sensing is a hard problem. There is no one-size-fits all solution. Odemetry (wheel encoders) provide a cost-effective method to measure relative position. however suffer from wheel slip and errors are accumlate over time. GPS only works outdoors, effected by trees/buildings, and without a nearby basestation (for expensive DGPS/RTK) the absolute position error is several meters. IMU (accelerometers + gyros + Magnetometer) suffer from drift errors and noise error causing ``random walk'' when integrated. Magnetometer are effected by magnets, are slow to respond and measure magnetic north (not true north). Infrared are cost-effective, but short range and saturated by sunlight. Ultrasonic range sensors are cost-effective and good for detecting large objects, but can't detect glass/water, only measure a few metres, have a wide beam and provide medium accuracy. RADAR uses radio (instead of sound) to detect objects at long distance, but are relative more expensive then ultrasonic range sensors. Image sensors (video cameras) are a cost-effective, rich in information, and two or more can get depth information, but are computationally expensive, hard to process the data (aka computer vision), affected by dust/fog/rain, and light variations. LIDAR are high accuracy (about 1mm), however are expensive (prices are coming down every year), but can't detect glass/water. Distance measurement sensors are easy to interpret, other sensors are hard. Colour constancy and object classification is very hard (e.g. “Is it a tree or a human?”).
We have a deep understanding of control systems. We typically use Linux-based SBC's and a program a custom PID controller - perhaps even a cascade PID controller, bayesian filters, particle filters, kalman filters, Monte Carlo methods, or train a deep neutral network. The outputs of these systems might control various types of motors (e.g. brushed, brushless, servo, steppers) and/or various types of actuators (e.g. linear, pneumatic, hydraulic), and/or other things like lights or speakers.
We have been involved with computer vision and machine vision since early 2000's - we where involved in the development of two computer vision libraries before OpenCV became popular. Have done much biologically inspired techniques such as optical flow. Was involved in the early days of artificial intelligence using techniques such as Local Binary Patterns (LBP) and Haar-like features (HAAR). These days we typically use machine learning methods such as designing and training deep neural networks (outstanding for vision-based object recognition using ImageNet).
We where involved in the development of a robotic operating system which had 364,578 lines of code, before ROS was written.
We have developed custom software for various manipulators, and have a good understanding of forward and inverse kinematics.
We appreciate that challenges with robotics - particularly with robot navigation, computer/machine vision, and manipulation with the real-world, in real-time using real-robots.
Whilw we can custom develop robot navigation solutions. We can fast-track robot navigation solutions for ground, air and water based platforms via off-the-shelf autopilots. There are many about these days. Some cost-effective open-source options include Pixhawk 4, PX4, and ArduPilot. Some expensive closed-source options include Auterion Skynode, Tersus AutoSteer, Embention Veronte, Outback eDriveX and Trimble EZ Pilot.
We are confident with a broad range of skills and confident our Canberra Robotics Prototyping services can offer solutions such as:
These technologies can be used for various applications such as:
Canberra is the capital city of Australia and with a population of over 332,000, is Australia's largest inland city. The city is located at the northern end of the Australian Capital Territory, 280 kilometres southwest of Sydney, and 650 kilometres north-east of Melbourne.
The site of Canberra was selected for the location of the nation's capital in 1908 as a compromise between Sydney and Melbourne, the two largest cities. It is unusual among Australian cities as an entirely purpose-built, planned city.
Following an international contest for the city's design, a design by Chicago architects Walter Burley Griffin and Marion Mahony Griffin was selected and construction commenced in 1913.
The city's design was heavily influenced by the garden city movement and incorporates significant areas of natural vegetation that have earned Canberra the title "bush capital". Although the growth and development of Canberra were hindered by the World Wars and the Great Depression, it emerged as a thriving city after World War II.
As the seat of the government of Australia, Canberra is the site of Parliament House, the High Court of Australia and numerous government departments and agencies. It is also the location of many social and cultural institutions of national significance. The federal government contributes the largest percentage of Gross State Product and is the largest single employer in Canberra (although it is no longer the employer of the majority of working Canberrans, as was once the case).
Before European settlement, the area in which Canberra would eventually be constructed was seasonally inhabited by the Ngunnawal and Walgalu tribes. The Ngarigo lived south-east of the Canberra area, the Gundungurra to the north, the Yuin on the coast and the Wiradjuri to the west. Archaeological evidence from the Canberra region suggests human habitation of the area for at least 21,000 years. The word "Canberra" is derived from the name of the local Ngabri people dialect, one of the Ngunnawal family groups, from the word Kanbarra meaning "meeting place" in the old Ngunnawal language. The Ngunnawal name was apparently used as a reference to corroborees held during the seasonal migration of the Ngunawal people to feast on the Bogong moths that pass through the region each spring.