A Sample Process to Generate 4-Dimension Global Maps

Here is a sample process for creating an internal version of the local 4-Dimension Global Map and then integrating it with 4-Dimension Global Map. 

(As always, my proposals are meant to stimulate innovation, and I don’t feel a proprietary interest, the best designs should win, and I anticipate that new designs will continue to emerge even after initial implementation – that’s the nature of technology innovation.)

1. Start with Static Maps, if available, (roads, buildings, floor plans, and including locations of navigation beacons and other fixed points)

2. Use sensors to collect data about the local environment

     a. Each Autonomous Object will have its own sensors

     b. This may include “simple” data sent from objects that identify themselves, such as RFID (i.e., non-C⁴, see below)

3. Based on sensor data, create internal version of local 4-D map

     a. Each Autonomous Object will have programming to interpret the data from its own sensors and to generate its own internal version of a local 4-D map

     b. Note, each Autonomous Object is responsible for its own actions, thus it needs to always have its version of a local map, even if it decides to use external information to augment this local map to make decisions.

     c. Initially the internal version of the local map is static but it becomes dynamic as sensors gather data over time and the data are integrated into the internal version of the map

4. Join local Cloudlet Computing, Communication, and Control or C⁴ (pronounced “See-Forth” J)

     a. Note, when not part of a local Cloudlet each Autonomous Object runs its own Cloudlet

5. Obtain Local 4-Dimension Global Map from the Cloudlet

     a. One of the main purposes of C⁴ is to generate, update, and store the local 4-Dimension Global Map – this is a complex topic, so I’ll discuss this and subsequent steps in more detail in the future

     b. If there is no local cloudlet, i.e., there are no active Autonomous Objects in the vicinity, then request stored local 4-Dimension Global Map via the network; note the date on this map will help indicate how out-of-date the information is, and thus help in determining how to use it (e.g., static items may be relatively reliable, but dynamic items will be increasingly questionable the older the 4-Dimension Global Map)

     c. If there is no stored local 4-Dimension Global Map, this Autonomous Object’s internal version of the local map becomes the new 4-Dimension Global Map by default

6. Compare Local 4-Dimension Global Map with internal version of local

7. Identify discrepancies and use protocols to work with other Autonomous Objects in the Cloudlet to update 4-Dimension Global Map

     a. Our Autonomous Object will be missing data due to limited Point-of-View, limited accuracy of sensors, short observation time, etc. so in general the 4-Dimension Global Map will contain both more data and more accurate data

     b. One challenge is to relate the items in the 4-Dimension Global Map with those in the internal local map

8. Use the consensus 4-Dimension Global Map

9. Continue to update the process: sensor data, internal local map, and participate in Cloudlet to update 4-Dimension Global Map

10. If important discrepancies cannot be resolved, initiate a “Management Resolution” procedure

     a. Depending on the seriousness of the discrepancies, appropriate actions will be taken, such as stopping and waiting for resolution