Navigation 3: GPS and Map Race

Introduction: Navigation with GPS systems has definitely become more mainstream with the option of dashboard systems. Most new vehicles today have options for turn-by-turn navigation giving the appearance that GPS systems are rarely wrong. GPS systems in vehicles are more reliable than handheld systems but still need to be used with common sense. Handheld systems are dependent on strong satellite signals. With weak signal due to cloud cover, dense vegetation, or tall structures, accuracy can be very limited. When navigating with a GPS, it is always good practice to have a map also as a reference in cases of low satellite signal or lost signal. For this navigation exercise, a Juno GPS unit and a map were used to navigate all 15 points of the Priory Navigation Course. To add a little incentive, the team who completed the course first received a prize, but all teams were armed with paintball markers. If a team member was hit with a paintball, a 30 second penalty was instituted.

Methods: Using the Priory geodatabase from Dr. Hupy and the maps created for the map and compass exercise, a new base map with all 15 navigation points of the course were added. Then using the digital elevation model provided in the geodatabase, the “slope” tool was used to find areas where the topography had the highest change in slope degree. Being a race, the group wanted to avoid running up and down steep slopes through the woods instead of find the points below and on top of the slopes first. The output feature class of the slope tool was then reclassified using the “reclassify” tool. This allowed areas with similar degrees in the slope to be grouped together and symbolized. The 10  default classes were simplified to 3: high, moderate, and low (Figure 1).  

Figure 1: The slope reclassification from low (green) to high (red) degree is displayed at 70% transparency under the planned path of travel. The starting point (5) was assigned but from there, the path was determined both by nearest point and the path of least resistance either slope change or knowledge of vegetation type. 

After we identified where the areas of the highest sloping terrain were and how to avoid them, we plotted our path starting at the assigned “point 5”.  To test the accuracy of the reported locations of the 15 navigation points, each team had to collect GPS data for every point they reached. In the map document in ArcGIS, an empty point feature class was created to hold the collect GPS points. This feature class needed a point number field, and our group chose to set a domain to restrict the accepted values to 0-15 since there were only 15 possible points. The path, navigation course points, and empty point feature class were then deployed to a Juno GPS which was used as a guide when running the course at the Priory. A paper map was also printed. The reclassified slope feature class, point feature class, navigation path, large Priory paths, and no shooting zones were included to help monitor progress and aid in navigation when GPS strength was low. As an added attribute field for the path feature class, the pace count from each point to the next was calculated in ArcGIS. Right click on the new field in the attribute table and select "calculate field" from the drop down menu. Using the “shape_length” field in the path attribute table, the new pace field was calculated by (shape_length)*(64/100) since the pace count in 100 meters was 64 (Figure 2).  By having each pace count listed on the side of the paper map, this saved time trying to calculate each in the field and still gave a reference on how far we were to travel.

Figure 2: After the new "pace" field was added to the path feature class, the field was calculated with the expression shown above. The shape_length field automatically calculated by ArcGIS was used as a base distance from point to point, then the pace count in 100 meters was used to convert that point-to-point distance to a pace distance. 

Results: Our team navigated the 15 point course in about an hour and a half resulting in a second place finish. The GPS display was helpful to keep on path and the calculated pace count helped keep us within the correct distances. The collected GPS points by the team matched the original navigation points fairly well with the exception of 7, 11, 14, and 15 (Figure 3). Using the measuring tool in ArcGIS, the points collected by the team were measured at most 15 meters different from those reported.  

Figure 3: The collected GPS points are represented by the orange boxes and original point locations are represented by the red circles. Points that were the most different were those in locations with dense vegetation or high variation in slope values. 

Discussion:  

Navigation: The path created by the team worked fairly well in the field. The first half, from points 5 to 3, were all found with little exertion from walking through the woods; however, points 3 to 1 were located in terrain that held gullies and high ridges that were unavoidable. Having this at the end of the course was probably not the best idea, but since we were assigned point 5 to begin we really had no choice.  Point 2 was the hardest location to collect since it was down in the bottom of a steep, deep gully (Figure 1). Obviously the high ground is the place to be in a fire fight, so we nominated a single team member to run down with the Juno to collect the point. No casualties resulted.

Paintball Gear: Carrying a paintball marker while wearing a mask that is fogged up isn’t the easiest way to navigate a course like this. The markers were heavy since the hoppers were high above the top of the marker and the air tanks were bulky in the back. The masks were hot and fogged up after a while, making readability of the map and GPS difficult and walking through the course. The fog also effected how well you were able to see other teams to avoid getting hit and serving the 30 second time penalty. You also had to be fairly close to the opposing team during fire fights to be able to hit them. The trajectory of the paintballs was fairly erratic making accuracy only a wish. The masks weren’t all bad though. They did help by protecting your face from branches and buckthorn while walking through a few of the dense areas of vegetation. 

Juno GPS: The GPS units did make traveling and navigating the course quicker than just the map and compass as previously used. The visual display of the route the group was traveling with the display of our current location allowed us to travel and adjust accordingly to stay on course. When navigating using compass bearing, once you are off the line from point to point, it is difficult to arrive in the correct location to find the navigation point in question. The Juno wasn’t perfect however. Several times, 4, we had to reset the system due to bad satellite signal or hitting a button while running through the woods or shooting our paintball markers at a rival team. The satellite signal could also have contributed to the variation in the point location noted earlier. The points that displayed the largest discrepancy were those in areas where the terrain varied more so than the surrounding areas and the vegetation was dense (Figure 3). The trees, although still without leaves, create an obstacle for satellite signals to be received through making the accuracy of the GPS limited. Also, to reduce the display time for the GPS, we removed the base map from the display. Without having the GPS draw the image each time we moved, this saved display time however we were more dependent on the paper map for reference, which isn’t a bad thing. Carrying the Juno also was a bulky nuisance at times. Between trying to track the path our team was taking to the next point and watch for rival teams, the GPS became a vulnerability.