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 Mudgeeraba 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 Mudgeeraba Robotics Prototyping services can offer solutions such as:
These technologies can be used for various applications such as:
Mudgeeraba is a suburb of the Gold Coast. Mudgeeraba is a remnant of the type of township that characterises the rural hinterland of the Gold Coast. Subdivision of land was conventional and buildings were traditionally rural or rural commercial. Mudgeeraba was, like Nerang, an early centre, which rose to some prominence with the coming of the railway. During the more recent boom periods Mudgeeraba has become very fashionable with many large homes on acreage as well as many more affordable family homes in the area. Mudgeeraba contains important evidence of its earlier form and building and most older houses are situated on large blocks of .5 to two acres. Contained on an 'island' within the flood plain its essential character remains one of a nineteenth century village despite the recent encroachment of housing estates to nearby land.
Mudgeeraba also situates Somerset College, a highly aclaimed school.
"Mudgeeraba" is an Aboriginal term meaning "place where lies are told".