Research areas
In addition to providing engineering, solutions, and development services to our clients, we also conduct our own internal research. We strive to stay on the cutting edge of technology and develop custom solutions and systems. This page summarizes some of the general research areas we participate in. We always welcome collaborations and partnerships in these and other areas!

We've also provided access to some of the papers published by our team members, where the research has been sponsored by Aptus.

Space Robotics
A lot of sponsored research has been conducted in the field of space robotics by Narendran Muraleedharan (Naru) - a founder and core developer at Aptus. The research is focused on using model-free or model-minimalistic methods to emulate a free-floating environment using a robotic platform. Such an emulated environment can be used for extensive testing of space robots on Earth prior to launch. The system allows for testing of orbital control systems, attitude systems, camera systems, docking procedures, and other sub-systems. Testing these systems on Earth saves companies and space agencies billions of dollars in failed launches.

Complex dynamic coupling between the satellite bodies and robotic manipulators on space robots make it very difficult to accurately emulate movement of the robot. Generally, Hardware in the Loop (HITL) simulation is used, however accurate knowledge of the system model and dynamic parameters is necessary for such a simulation. This research proposes a gravity compensated force-feedback control method and a center of mass regulator that allows for the emulation of a free-floating environment with minimal (for spatial) or no (for planar) knowledge of the system model or dynamic parameters.



Following are a few of the publications that resulted from this research venture.
Recreating Planar Free-Floating Environment via Model-free Force-Feedback Control
Presented at the IEEE 2016 Aerospace Conference
[1.6 MB]
Development of an Emulated Free-Floating Environment for On-Earth Testing of Space Robots
Master of Science Thesis by Narendran Muraleedharan
[22.8 MB]
Experimental Validation of a Planar Free-Floating Emulator via Model-free Force-Feedback Control
Presented at the IEEE 2018 ICMA Conference in China
[520.2 kB]

Spherical Robots
Generally, mobile robots use differential drive for locomotion. However, differential drive robots are not favorable for exploration in rough terrain due to the possibility for wheels to get stuck in harsh environments. Spherical robots have been tested in the past for exploration and locomotion in uneven terrain.

Spherical robots have used various different methods of locomotion. These include a weighted pendulum, internal drive (like a hamster ball), control moment gyroscopes, and momentum wheels. One of the simplest methods for locomotion is using a weighted pendulum. In the past, companies have used a drive-and-steer system with the internal pendulum. However, such a system does not allow for omnidirectional locomotion - which is one of the most attractive features of a spherical robot.

This research focuses on developing a control system that allows for omnidirectional movement. Such a controller was developed and the results were presented at the IEEE 2016 Southeast Conference in Norfolk, Virginia.



Below is a publication on the designed omnidirectional controller for spherical robots.
Omnidirectional Locomotion Control of a Pendulum Driven Spherical Robot
Presented at the IEEE 2016 Southeast Conference
[955.5 kB]

Traffic Flow Optimization
A recent project undertaken by the Aptus team in collaboration with Bjorn Forsdal, is aimed at improving traffic flow with the use of smart intersections and artificial intelligence. Many intersections are already equipped with traffic sensors including cameras, trip sensors, and weight sensors under the road. This research explores various options for the use of artificial intelligence in order to optimize the traffic flow pattern and minimize commute times.

We have developed a traffic simulator in order to test custom systems. We're training a Long Short Term Memory (LSTM) neural network with auto-generated training data and brute force optimization in order to produce more effective logic for traffic light sequences. We are also experimenting with genetic and evolutionary algorithms to train the neural network.

This is an ongoing research project. More information on the project shall be posted as it becomes available.

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