Simulation of a Dragonfly Intercepting its Prey Victoria M. Hurley Capstone Project, Union College Advisor: Prof. M Rudko Client: Prof. R Olberg March 5, 2005

Presentation Overview Project description & purpose Background Design Results Discussion Questions My presentation will follow this format: starting with the description of the project. Then I’ll review some background information, go over its design and present the results. I’ll finish with a short discussion and open up to any questions.

Mission Simulate dragonfly pursuing prey Why? How? For senior year I concentrated on creating a program that simulates a dragonfly pursuing its prey. The first question I asked was why focus on making a simulation of this behavior? Though Prof. Olberg and other scientists have spent a great deal of time studying this behavior, many questions are still unanswered, such as: what factors truly govern the success and failure of these pursuits.?To what extent does each known factor influence the behavior and what are their constraints? Are there aspects of the behavior scientists are overlooking? The next major question in the project was how to create an accurate simulation. This required collecting and researching the various parameters that are believed to govern a dragonfly’s flights.

Background To begin the background, most importantly dragonflies do not track their prey. Unlike the cheetah who trails after its prey, the dragonfly projects the location of the prey and intercepts it there. This is clearly visible in the following high-speed movie clip taken this past summer.

Background Here is a graph of that same video pointing out the specific location of the key points. As you can see the dragonfly clearly intercepts the prey as opposed to tracking the prey.

Background Acuity zone Constant prey angle Speed Turning capabilities Latency The specific factors of the flight include the following: acuity zone, constant prey angle, latency, speed, and turning capabilities. The acuity zone for a dragonfly is the section of the retina that is populated with highly sensitive receptors, omatidia, that are specifically used for viewing prey. One can think of it as a flashlight and the area which the flashlight lights brightest is where the acuity zone is directed. To pursue a prey the insect must be in the acuity zone. Furthermore, the image of the insect on the retina is to remain constant. If the dragonfly maintains the prey at the same angle or location on the retina, the two paths will intercept. However, due to the neurological makeup of a dragonfly, latency must be included. The first delay occurs at decision making. When the dragonfly decides to pursue its prey the first movement will follow approximately 100 milliseconds later. Similarly, when prey makes a drastic movement (such as change direction) the dragonfly will respond approximately 40 milliseconds later. The range of the speed a dragonfly flies at is required in order to move the animal along at the proper rate. Finally, the turning capabilities of dragonflies. When a prey changes direction the dragonfly also turns. This behavior should also be simulated.

Final Design Algorithms Prey path: Spline vs. xlsread Prey angle: trigonometry tan theta = (m2 - m1) / (1 +m1m2) Next location: trigonometry (SohCahToa) Rotating: trigonomtery Speed: randint After weeks of coding, tweaking and altering I completed the simulation program. The following is a list of the algorithms I used or choices I made. First, the prey path could be generated in two ways: using Matalb’s spline function or simply reading in the excel file containing the coordinates. If a goal for the simulation was to create original prey paths then the spline function would have been helpful. However the client simply wanted to re-construst the videos captured this summer so I chose xlsread. The next major decision concerned calculating the angle between the dragonfly and the prey. The most straightforward option is to use trigonometry to determine the angle between two lines. I also used trig to calculate the location of the dragonfly in the next frame and to rotate the dragonfly when turning. Since the speed of a dragonfly varies throughout a flight, I utilized Matlab’s randInt function to randomly assign the velocity of the dragonfly.

Final Design Inputs File vs GUI Prey path Dragonfly parameters File load, save, print The user interface was created using Matlab’s guide. This single screen provides the user with a number of options. Here they are able to chose the excel file containing the prey coordinates. Then they can enter in all of the dragonfly parameters or leave the default values. And they can chose where to store the movie file and what to name it. The user can save their data for another time and use the load section above to reuse those parameters.

Results Being a simulation, the results of the project are seen in the movies created. Here is an example of a simulation. As I will prove later, the simulation takes a long time to generate so I will just show the resulting movie. (Run sim) here the blue dot is the path of the prey. The green circle and red stat represent the front and back of the dragonfly head. When the dots become light blue - that marks the points the dragonfly saw and responded to. In the end you can see that the dragonfly begins to turn in toward the prey that it sees when in reality the prey is further to the left. (click) Here is the actual flight of the dragonfly. We can see that in this simple prey path the simulation is quite accurate.

Results This second example uses a more complex prey path. Though this would not be a realistic prey path, it shows the capabilities of the simulator. Clearly the real dragonfly uses some form of problem solving in order to successfully capture the prey.

Discussion What does it tell use? Problems? Improvements? Accurate acuity zone? Decision making: rotating vs. turning Calculating distance - unknown Problems? Improvements? Missing behavior 3D Run speed The comparison of the simulation to the real dragonflies brings about a number of points. First, the acuity zone. We chose parameters that may be too restrictive. This could be due to the fact that in reality the dragonfly pivots its head to place the prey in the acuity zone. However the simulation keeps the head stationary. Also, as seen in the last video, we find that the dragonfly does some form of problem solving. Our simulation lacks the decision making ability: does the dragonfly stop abruptly and rotate, slowly bring about a turn or hover before continuing its pursue. The major reason for the lack of problem solving is because scientist do not know how the dragonfly calculates distance. Without being able to mimic their distance estimation, we can not accurate implement their decision making. In terms of problems and improvements, the statements above generally capture the missing behavior in the simulation. Also, we must remember that the simulations and videos are capturing a 3D world in two dimensions, which brings about the obvious issues. On the other hand, a major improvement to the program would be to decrease its speed.

Discussion This is a graph of the run time of the program. When running this program without AVI creation it completes in about 5 to 10 seconds. However, when creating an AVI the finish time runs between 5 and 11 minutes. On one side you have a video that will last forever and can be played over and over. Yet on the other side it can take a long time to find the right parameters that create the desired video.

Questions? And that concludes my presentation. For fun I thought I’d include this video of a dragonfly missing its prey (a bead).