|Digital Signal Processing Modules|
|Unmanned ground vehicles|
|Student Science Projects|
|Education and research in the exciting field of robotics|
|Including swarm intelligence, emergent computation and control, neural networks,|
|genetic programming, other fascinating biologically inspired technologies|
|Lumenosys Technologies Are Rooted In Futuristic Engineering
The development of real-time embedded systems, such as those of industrial, space-based, and
military robotics continue to face increasing system complexity and requirements for higher levels
of autonomy, performance, and dependability.
For those producing robots and robot control systems for hobbyists and hobby stores, our solutions
enable rapid development of resilient distributed control systems, drastically reducing time to
market while improving quality and reducing the need for low-level validation and testing. Our
products allow you to focus more on developing your robotics application and less on fragile
For education and research our system solutions provide a complete environment to explore new ideas in robotics
and control and quickly put them into action. Using familiar graphical engineering tools and our system solution,
students can put their knowledge into motion with real hardware, but without the integration headaches to
distract from the learning process.
Researchers can utilize our distributed platform to investigate new ways of building systems which leverage
inspiration from biological systems, such as swarm intelligence and evolutionary computation -- enabling
performance and resilience never before demonstrated by man-made machines.
More and more robotics systems are finding their way into safety critical domains such as unmanned aircraft
(UAV), automotive, and industrial controls, where reliability and safety demands thorough system validation and
test coverage, including meeting demanding certification requirements such as DO178B and IEC61508.
Utilizing industry proven model-driven engineering tools, our system development solution will enable you to quickly
get started and focus on your robotics application significantly reducing the learning curve, yet enabling you to
develop highly complex and fault-tolerant systems with improved tractability and testability with reduced risk.
Unlike some software or hardware-only vendors, we provide complete integrated systems including ruggedized
embedded processors, intelligent sensor and actuator interface modules, software framework and support libraries,
and integrated model-driven tools for design, integration, and hardware in the loop test.
OUR PRODUCTS ARE THE RESULT OF YEARS OF RESEARCH AND DEVELOPMENT IN ROBOTICS,
ROBOT CONTROL SYSTEMS, AND OPTICAL COMMUNICATION WITH FOUNDATIONS IN SCIENCE
Robots on the Ground: Unmanned Ground Vehicle (UGV)
Unmanned ground vehicles have been developed to operate only on land and are used to do the dirty, dull and
dangerous jobs in factories, in outer space, by the military and for fun by the hobbyist. New control systems and
innovations are making the UGV a valuable asset for all areas of modern endeavor.
UGV's are already being used in civilian operations as checkpoint guards and for grounds surveillance.
Firefighters use them to detect explosive chemicals in industrial fires, police agencies use them to defuse bombs
and with cameras onboard they can scout for dangerous criminals hiding in buildings. Unmanned ground vehicles
have also been used extensively in space exploration, crawling around on the surface of the moon and Mars
operating autonomously without a human operator.
There are two main types of unmanned ground vehicles: (1) autonomous (2) remote-operated.
All of the movements and actions of this type of robot must have the control of a human, which is frequently done
through a communications link. The human operator either views the environment directly, or uses a camera
to remotely view what is occurring. The human operator makes the decisions. They are used in medical and
surgical arenas, by the military and in factory environments.
Scientists and engineers who are currently creating ground robots want them to function like a human, but without
the frailty of a human mind and body. A robot that is “autonomous” does not need a person to operate it, but more
importantly, it must also have the human ability to actually “learn” from its experiences and mistakes. This is an
important goal for many designers.
Autonomous robots have the ability to acquire information about from the environment and especially the space
around it. It does not require constant human intervention can work for extended periods, travel a specified route
without human assistance, avoid harmful situations, and can actually repair itself.
Science Fiction or Fact
The ultimate goal is to develop a robot which can acquire new capabilities without human intervention as the
environment around it changes, and to adjust to and actually develop new strategies to deal with a changing reality
as a human would. This perfect robot would also perform its own maintenance and repair. The new innovations
currently being studied tells us this is just around the corner. For more on Robot Control Systems See
Robots on the Ground
Robots in the Sky: Unmanned Aerial Vehicles and Control Systems
Important advances in robotics during the last decade have increased their value to military and civilian operations,
especially with the development of unmanned aerial vehicles. Miniature UAVs (unmanned aerial vehicles such as
small helicopters) have become increasingly sought after for military tactical operations and for commercial
UAVs are being used for surveillance by law enforcement and border patrol agencies and the National Oceanic
and Atmospheric Administration (NOAA) is using UAVs to gather hurricane data to predict and warn of potential
damage to communities. New Directions in UAV Development
Bird-sized UAVs and in the future, insect-sized UAVs have been made possible through the reduction in size of
on-board communication, avionics and sensor hardware.
(UAVs) can be divided into 1) Fixed-wing UAVs (unmanned aerial vehicles) which need specific velocity to stay aloft
and a runway for take-off and landing, and 2) Rotocraft UAVs (unmanned aerial vehicles) such as helicopters.
Helicopter rotocraft UAVs (unmanned aerial vehicles) have distinct advantages over fixed-wing, because they do
not need a relative velocity to produce aerodynamic lift forces allowing them to take off and land vertically.
Small scale helicopters (<5kg) have all of the physical principles and flight capabilities of full sized UAV helicopters,
but they are more agile than the large unmanned aerial vehicles. This latter fact and the efficient autonomous flight
platforms they have, as well as their lower developmental costs, have brought them to the attention of the UAV
(unmanned aerial vehicle) research and development community.
Designing the small autonomous flight platforms for the UAV helicopter requires knowledge and experience in
diverse fields of engineering. One of the biggest challenges is the development of sensor integration and sensor
fusion, improvements in flight controller design, and better communications and path planning.
For more information and the latest scientific research articles on UAVs and unmanned aircraft control systems
Robotics, UAVs and Control Systems
The study of swarm intelligence based on the swarm behavior of birds and insects has been applied to intelligent
robotic systems creating the study of swarm robotics.
Swarm robotics is an emerging, innovative approach to utilizing them by having a large number working together
in a coordinated effort to accomplish a single goal. This concept developed from biologically inspired technologies
based on emergent computation theory (see Emergent Algorithms below).
The goal for the future is to create robots that as a group will interact as ONE in an autonomous, intelligent manner
with each other and with their environment, and that this will cause a collective and appropriate behavior to emerge
spontaneously as it does in a swarm of bees encountering danger to the group.
The behavior of a large number of them, like a swarm of bees, must account for the behavior of each android which
is changing in response to the behavior of the other robots and whose behavior as a group is reacting in
accordance with a predesigned goal.
Just as in the communication between bees in a swarm, the functioning of a swarm of androids is dependent upon
continuous feedback between each android. This is usually best accomplished with a wireless method of
communication, such as through radio communications. Algorithm control strategies have been developed to allow
them when in a swarm to communicate and function at the local, decentralized level, much like a swarm of bees.
One of the problems of developing an efficient algorithm for swarm robotics is the dynamic notion of agreement, as
well as their coordination and synchronization. Another issue that scientists are grappling with when writing
algorithm for a swarm of androids are the errors that occur in the communications between them.
One important communication concept in controlling multiple androids is the algorithm called “robot flocking.”
Flocking algorithm enables a them to swarm, move in formation and to preserve this formation while moving.
For additional information on flocking see Robot Flocking
Why is it important to have a swarm of androids? While swarming in a formation they have many important
applications, such as transporting large objects, exploring hazardous areas and surveillance.
NEW THEORY CAN BE QUICKLY APPLIED TO REAL WORLD ROBOTICS
Scientists are beginning to see that although the concept of emergent computation occurs in nature, and that it can
be used to study natural patterns, it can also be used in other areas of phenomena to develop solutions to specific
We used this concept in developing our optical communication and security innovations. We have utilized the latest
in advanced sensor and energy efficient computing technologies to develop rugged small form-factor components
for building advanced fault tolerant control systems for robotics. Future innovations in robotics and optical
communication technology are being developed using the biology-based concept called emergent computation or
What is the Concept Behind Emergent Algorithms?
Emergent computation is a paradigm inspired by biological systems in which complex global behavior arises from
the local interactions of large numbers of simple components. The concept of emergent computation has led to the
formation of emergent algorithms which are used to solve problems in constructive interoperability systems,
including swarm robotics and may eventually be used to solve complex issues within the Internet.
Exploiting this paradigm for complex engineering systems offers significant advantages over conventional
centralized software and hardware systems since the algorithmic complexity is achieved through simple
components, each implementing simple rules, with well-defined interfaces and easily testable functionality.
The issues resolved by such systems can be solved in a massively parallel fashion and offer the possibility to
exploit redundancy and fault tolerance.
In a complex computing or communications system based on emergent algorithms, the output of
a system results from an autonomous decision made by each part, based on its own interpretation
of the data or information.
For instance, like a bee communicating to others an intelligent decision is made by the swarm, based on information
from its parts, while no central information processor is present or needed.
Theorists in many fields are excited by this new concept. In computer engineering, it suggests that the computer
not only can play the role of an autonomous agent in decision-making situations categorized as routine but even
in some novel situations.
These learning models can be utilized in the exploration of the solar system and in social environments
characterized by abundant data. Although the former is understandable, given the autonomous nature of probes
traveling to the far reaches of the solar system, the latter is somewhat more challenging to envision.
One possibility is in the realm of highly complex systems that not only demonstrate emergent properties such as
adaptation but also those characterized by the inability to effectively reduce their form, processes, and outcomes
to analytically ordered models.
Emergent computation is potentially relevant to these areas, including adaptive systems (complex social regimes
under policy interventions), parallel processing, and cognitive and biological modeling, and other communication
and software systems.
Many biological systems appear to carry out this type of distributed computation-- for instance, ant colonies,
nervous systems, and immune systems. One favorite example among biologists is slime molds, which exist for most
of their lives as single-celled, amoeba-like creatures.
When the food supply of the slime mold runs dry, they somehow figure out, through local signals between cells,
how to swarm together into a slug-like, multi-cellular organism that produces the spores that give rise to the next
Emergent Algorithms Are Characterized by Decentralized Control
Unlike traditional algorithms, in which a central processing unit carries out programs, distributed emergent
algorithms lack a central controller. Instead, large numbers of simple units interact with each other to achieve
complex, large-scale computations.
Plants seem to engage in distributed emergent computation, although they don't add, subtract, multiply, or divide,
they do appear to compute solutions to problems of how to coordinate the actions of their cells effectively.
Researchers believe plants may use computation to figure out how wide to open pores in their leaves. The leaf
pores, also called stomata, open to allow in carbon dioxide, which plants need for photosynthesis. However, open
pores also let out water and so may dehydrate the plant. To balance these competing factors as environmental
factors change, plants constantly adjust how many and how widely their pores are open.
The way that plants achieve this balance has been a mystery. There's no brain to coordinate the tens of thousands
of pores, and individual pores seem to have no way of knowing what distant pores are doing.
At first, biologists thought that each pore simply decided independently what action to take. About 10 years ago,
however, researchers noticed that large patches of pores frequently open and close in concert. More recently, Keith
Mott, a biologist at Utah State University in Logan, discovered that over minutes, these patches of synchronization
move about the leaf; often displaying complex dynamics.
He described these observations to physicist David Peak, a colleague at Utah State. They reminded Peak of
patterns that turn up in cellular automata, a kind of distributed emergent computer.
"It occurred to us that the patterns could be symptomatic of a distributed emergent computation," Mott said.
Mott and Peak next investigated whether there was more to the seeming similarity between the behavior of leaf
pores and of cellular automata. A cellular automaton consists of a collection of units called cells, each of which can
be in one of several states. Over time, the cells change their states according to rules that depend on their current
states and those of their neighbors.
The best-known cellular automaton is the Game of Life, invented in 1970 by British mathematician John Conway,
now at Princeton University. The game consists of a grid of cells, each of which is considered to be either dead or
alive. At each time step, some cells switch state according to simple rules.
An example of this is when a live cell with at least four living neighbors dies of overpopulation. Even though each
cell is influenced only by nearby cells, complicated global patterns can emerge. Cellular automata may underlie
nearly all phenomena, from the physics of elementary particles to life and intelligence.
Although, most scientists don't subscribe to such a sweeping concept, they are using principles of emergent
computation to process images, and model earthquakes, traffic patterns, ecological patterns and the migration of
animals, as well as neural circuits and the patterns of tumor growth.
Science of Artificial Intelligence (AI) and Nature
As in the study of emergent compution, scientists are utilizing the structures and mechanisms found in
nature to futher develop the science of artificial intelligence systems.
AI is basically the theory and use of machines that act intelligently. In what ways could a machine be intelligent?
They can be designed to recognize faces, negotiate rush-hour traffic, learn to fold clothes and of course calculate
better than humans can.
Nature–inspired techniques in the field of artificial intelligence (AI) correspond to methodologies that are based
on how biological systems and natural networks deal with real–world situations in nature.
More specifically, some of the main characteristics of nature-inspired algorithms are the following. They imitate
real–life networks in the way they function and evolve, e.g. Ant Colony algorithms. Natural ant colonies
cooperate in order to find a high–quality food source. In artificial ant colonies, artificial ants (agents) exchange
information in order to find a near–optimum solution. They also take advantage of some properties from natural
systems, such as DNA computing.
DNA strands are designed to encode real values by variation of their melting temperatures. The thermodynamic
properties of DNA are used for effective local search of optimal solutions using biochemical techniques, such as
temperature gradient gel electrophoresis.
A simple example that has been thoroughly studied in the past is neural networks, which take advantage of the
way that human brain functions and produce highly accurate solutions for real–world problems. The human brain
in fact represents a natural network.
Each neuron is an autonomous unit and at the same time cooperates with neighboring neurons. However, the field
of Artificial Intelligence expanded its research to other natural systems, too.
Researchers recognize and define as the main components of nature-inspired artificial intelligence five (5) major
categories of related techniques or approaches:
(1) Ant colony optimization algorithms (ACO)
The inspiring source of ant colony optimization is the foraging behavior of real ant colonies. Scientists are
studying the social behavior of real ants, methods of indirect communication, foraging behavior and how ants are
able to find the shortest path between food sources and their nest.
(2) Particle swarm optimization algorithms (PSO)
These are algorithms which model the social dynamics of bird flocking, fish schooling and swarm theory. An
example is to pattern how a flock of birds flock synchronously, change direction suddenly, scatter and regroup
and perch on a target at the same time.
3) Artificial immune systems (AIS)
This using the basic properties and mechanisms of the human immune system to perform complicated tasks,
such as the way the immune system can perform decentralized distribution, self-organize and the memory
mechanisms of antibodies.
(4) DNA computing
Scientists are attempting to design computers which will be based on DNA molecules, with the hope of either
replacing or beneficially complementing silicon based computers. DNA plays the role of memory in nature.
DNA is the genetic material containing the whole information of an organism to be copied into the next
generation of the species. DNA computing is a computational paradigm that uses synthetic (or natural)
DNA molecules as information storage media. Scientists are looking at DNA computing to see if they
can use some features and functions from real DNA strands. These include characteristics of the
polymerase chain reaction, gel electrophoresis, enzymatic reactions and the massive parallelism and
huge storage capacity.
(5) Membrane computing
Membrane computing makes use of the internal organization and functioning of living cells, as well as
their cooperation in tissues and higher order structures, as a rich source of inspiration for computing
Austin, R. Unmanned Air Systems: UAV Design, Development and Deployment. 2010.
Eberhart, R. C. Swarm Intelligence. 2001.
Grierson, D. E. Emergent Computing Methods in Engineering Design. 2010.
Larsen, J.J. World of Robotics. 2012.
Liu, Y. Biologically Inspired Robotics. 2011.
Valvanis, K. Handbook of Unmanned Aerial Vehicles. 2012.
Yang, Y. Fault Tolerant Flocking Algorithms for a Group of Mobile Robots: Fault Tolerant Flocking. 2011.
For additional information and scientific research articles on emergent computation and emergent algorithms
development Emergent Computation
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