Investors: Press Release
GeckoSystems Begins Licensing Discussions With Publicly Traded Robotics Co.
CONYERS, Ga., Feb. 8, 2010 -- GeckoSystems Intl. Corp. (PINKSHEETS: GCKO) -- announced today that they have entered into substantive technology licensing discussions with a U.S. robotics company. GeckoSystems is a dynamic leader in the emerging mobile robotics industry revolutionizing their development and usage with "Mobile Robot Solutions for Safety, Security and Service™."
Martin Spencer, President/CEO of GeckoSystems stated: "As all of us here at GeckoSystems are excited about this development, due to the nature of the upcoming discussions I feel it is in the best interest of all parties involved to withhold the name of this publicly traded company at this time. The potential outcome(s) of it is something that has been in the works for several years and is now coming to what looks to be an extraordinarily profitable culmination. I believe their interest in us is due to not only our flagship product, the automatic self navigation software, GeckoNav™, but also the reality that we have a complete multitasking personal robot, the CareBot™, with verbal interaction capabilities, GeckoChat™, and the ability to routinely follow a designated family member with GeckoTrak™."
The cost saving benefits of GeckoSystems' suite of mobile robot technologies will generate additional multiple revenue streams for GeckoSystems in the form of licensing, royalties, training, and sales of various software and/or hardware systems and/or subsystems beyond manufacturing and distribution of GeckoSystems' coming product line of mobile service robots. The CareBot™ is their first product, now in in home evaluation trials, to be realized from their suite of proprietary technologies.
"Hence our strongly held belief that given our extraordinarily efficient and very robust AI navigation engine, and its portability, we expect GeckoNav to also be important in other large markets such as professional healthcare, education, commercial security, public safety, agriculture, and defense. Our basebot technologies, such as GeckoNav, are not only relevant for consumer markets, but also several other business-to-business markets for improved ROI for our investors," concluded Spencer.
GeckoNav's Core Capabilities:
1. Subsumptive software architecture enabling cognizant navigation for unexpected obstacle (static or dynamic) avoidance while "on path" with the ability to resume path following.
2. Sensor fusion technology such that the GeckoNav is sensor loving. By utilizing multiple sensor systems (like a blind man listening and counting steps while using a cane, uses two senses --tactile and hearing-- to routinely navigate known, and unknown, environments) the GeckoNav's AI software architecture enables differing, high-count sensor systems synergy.
3. Short term AI memory software such that GeckoSystems' proprietary sensor fused, scanning CompoundedSensorArray may be fully utilized. Consequently, total cost for sensor systems cost is dramatically reduced.
4. Emergent behaviors expression (which are not pre-programmed) such as the left/right routine when encountering a dynamic obstacle that moves to the same side that the robot has chosen to use to avoid the now confounding obstacle. The robustness of this emergent behavior is apparent as the robot finally, after several left/right attempts, succeeds in avoiding the dynamic obstacle, and resumes path.
The resultant level of mobile autonomy can be likened to that of a "blind man with a cane in his own home" or "loose crowd capable." All GeckoNav source code is in C++ and is not hardware or OS centric.
Some Fundamental Issues of Automatic Self-navigation in Dynamic Environments
Background:
For any Mobile Service Robot (MSR) to have probable hope of utility, it must have the intrinsic
and timely ability to avoid unforeseen, dynamic obstacles and still reach its desired endpoints
or physical locations. Many MSR prototypes are limited by their navigation software architecture.
Historically, MSR architectures have been based on either a pre-set path following technique,
where the sensors are only used to detect failure of the preprogrammed path, or they have
used a purely reactive technique that has no concept of the larger world that the MSR inhabits
and cannot be used for useful tasks.
The path-following techniques suffer from being unable to adapt to changing conditions quickly or smoothly. The MSR basically travels blind until it is about to hit something, and once it has detected an obstacle, the resulting decisions required are very complex. As a result, the environment must be highly structured to avoid confusing the MSR so that simple decisions will suffice or a lot of computing power must be available to maintain and compute path alternatives. Requiring a highly structured environment reduces the usefulness and flexibility of such a MSR in a human environment. In addition, the need for a lot of processing power makes MSRs really expensive and their useful "on" time very short due to the power required for the "high clock" CPU or PC typically on board.
Further, the purely reactive architectures suffer from having little sense of past events, future goals, or of even where exactly the MSR is within the world. Typically such MSRs have no memory of the world that they have traveled and "live" only instant-to-instant. They may reach a particular destination, but it is by pure chance and the MSR will not be able to recognize that it has reached the desired destination without providing a modified environment (e.g. beacon techniques such as the legendary Arctec Systems' "Gemini," Evolution Robotics ER-1 and others). In its pure form, something seen in many toy robots, this technique is almost useless for true automatic self-navigation or tasks in a dynamic human environment. This kind of MSR is typically characterized by its use of binary IF-THEN rules like "If bumped left then turn right." Such an architecture does not scale for the multiple sensors required for Cognizant Navigation. Cognizant Navigation is the ability to find locations repeatedly upon request without hitting unexpected obstacles.
Cognizant Navigation is a non-trivial problem that has a number of facets. There must be enough sensor information of the right kind to not hit large obstacles such as walls, furniture, and people. There must also be enough sensor information to avoid smaller obstacles such as toys. Furthermore, the navigation engine must be able to react to quick local changes without losing track of its task. The MSR must also have a memory of where it is within the world and be able to repeatedly find locations within that world even if there are unexpected obstacles. This means that there must be enough processing power and RAM to accomplish this while still having enough battery life to stay active for many hours while performing useful tasks like vacuuming or carrying more than a trivial sized load. These important capabilities are the basic, required foundation for useful MSRs in a human environment. Until the CareBot, almost all consumer MSRs have fallen short in one or more of these areas.
Cognizant Navigation is much more than the simple reactive, bump-turn mobile robot behaviors seen in most traditional, or legacy mobile robots. Such a robot may reach the goal, but isn't "aware" that it is attempting to reach that goal and can't recognize it when located. Other legacy mobile robots blindly follow line segment paths like virtual train tracks and may be "aware" that they are trying to reach a goal, but they have problems when reacting to new situations that require deviation from the planned route due to their limited sensors and available CPU power. Typically, these robots cannot sense obstacles until they actually run into them!
Are these MSRs cognizant? Cognizant means to be aware or have conscious knowledge. The word "aware" implies the MSR remembers where it is, where it was, where it is "supposed" to be going, as well as being aware of immediate changes in the environment that may require a response. Humanlike short term and long memory management, along with enough sensor information, is the key to resolving this problem. Your existing PC has the raw computing power, memory, and data storage needed for robust personal MSR cognizant navigation, scheduling of areas to be vacuumed, and much, much more.
GeckoSystems's GeckoNav is different. Its Biological Hierarchical Architecture provides the benefits of both control and reaction within a single framework without the disadvantages of either technique alone. As a result, it is able to respond quickly and intelligently to short-term navigation situations while still providing the ability to guide the MSR toward accomplishing useful tasks within a map of the world that the MSR maintains. It turns out that this approach is synergistic and reduces the complexity of trying to "force fit" either of the other traditional solutions to solve the whole problem.
Biological Hierarchical Architecture is a GeckoSystems' proprietary MSR navigation software scheme incorporating several advanced artificial intelligence (AI) methods such that together vote on the best solution. It should be noted that "sufficient" sensors for navigating a home environment while avoiding unexpected obstacles is a critical prerequisite.
Sensors - Why Other MSRs Bump into Walls, Chairs, Tables, etc.
Many available MSRs are limited by their sensor count, position and/or interpretation strategy. MSR sensors such as bump switches, feelers, and whiskers have the problem that they cannot sense their environment without actually colliding with the world. Fixed single sonar and infrared distance (IR) range finders are an improvement, but individually they give very little information about the surrounding world. They may help avoid running into an obstacle directly in front of the MSR in one narrow direction, but they aren't very useful in helping the MSR navigate. There are too many directions from which unseen problems can approach, and even if the obstacle is detected, it is practically impossible tell the true extent of the obstacle and what the response should be from that single data point.
Even having many different kinds of these sensors does not necessarily solve this problem. The MSR must be able to assess the current space around the MSR to enable robust navigation, and to do that the MSR must have enough information of the right kind, not just many arbitrary sensors. Expensive research MSR manufacturers understand this need, and solve this problem through a very costly array of multiple sonar and IR sensors or even more expensive machine vision systems and/or laser rangefinders interpreted by either CPU intensive computations, or by quicker neural nets that can be easily over trained and become brittle in their ability to reliably discern fixed and/or moving, unforeseen obstacles.
GeckoSystems's solution to this problem uses high-data, low cost fixed ultrasonic rangefinders (sonars) and scanning IR sensors in an array called the CompoundedSensorArray™. The CSA can image the surrounding space in 250-275 different directions, not just one single direction like a single fixed sensor. This is actually more advanced than most research MSRs in this respect, which in contrast can generally sense between only 7 to 16 unique obstacle positions on the forward half of the MSR. This is an increase in resolution of 15 to 40 times over such MSRs! The reason for this is that such MSRs tend to assume and operate in fairly structured environments, like offices, empty campus hallways and contest mazes and as a result encounter fewer challenges in their environments. In contrast, GeckoSystems' basebot technologies have been designed and tested for typical home environments, which many consider to be the most challenging of all, from the beginning.