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The company has developed a real-time automatic navigation control system called GeckoNav. Fuzzy Logic and Subsumption architecture are used to enable the fully autonomous AI behavior for the Mobile Service Robot MSR to sense and avoid dynamic and static obstacles in its environment. The GeckoNav Architecture is fully expandable through a variety of standard interfaces. 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.

GeckoNav is GeckoSystems fundamental and proprietary AI software technology that enables automatic self navigation for their mobile robots. As a reference, or bench mark for the "update rate" or "reflex time," jet fighter pilots, selected for their extraordinary eye-hand coordination and intensely trained to be the "best of the best," is generally to milliseconds.

Running on a relatively low clock mhz x86 CPU that is common in most Windows capable computers on most versions of Windows which tend to slow down throughput since not a real time operating system and burdened with many required system calls GeckoNav updates in an astonishing milliseconds. This "reflex time" is times faster than a jet fighter pilot!

Using the old "Top Gun" movie and Tom Cruise's role in it as a metaphor, this means that GeckoNav is fast enough to not only pilot supersonic jet fighters, but also motorcycles and old Porsche Speedsters! In other words, since GeckoSystems AI mobile robot navigation software is times faster than Tom Cruise as a pilot, with appropriate and sufficient sensor information and fine control of the locomotion system, GeckoNav can drive just about anything Homes are the most difficult environment of all in which to self navigate.

They are poorly structured with numerous delicate stationary and moving obstacles. As demonstrated in numerous videos, GeckoNav works extraordinarily well in those environments.

Describing how "well" a mobile robot automatically self- navigates is difficult. We use metaphors to put into common understanding the application of this criteria: A wide receiver is very fast, very skilled at avoiding moving obstacles while at a very, very fast running speed to a predetermined point so they can catch the football being passed to them. So "wide receiver capable" level of autonomy would be able to pursue someone running, or evade someone running after them.

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, either 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. 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. 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. 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.

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.

Subsumptive software architecture enabling cognizant navigation for unexpected obstacle static or dynamic avoidance while "on path" with the ability to resume path following. Consequently, total cost for sensor systems cost is dramatically reduced. Levels of Autonomy Describing how "well" a mobile robot automatically self- navigates is difficult. Some Fundamental Issues of Automatic Self-Navigation in Dynamic Environments 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.

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