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Abstract. Introduction Motivation. Uniqueness of idea. Goal: Add a new challenge to the current paintball style of play. Introduction Application: Paintball games. Definition of terms:
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IntroductionMotivation • Uniqueness of idea • Goal: Add a new challenge to the current paintball style of play.
IntroductionApplication:Paintball games • Definition of terms: • Paintball games - games where there are two teams who’s objective is to capture the other teams flag and bring it back to home teams base. Opposing team members are marked with the use of a special paintball paint • Paintball gun - special airgun that projects paintballs. • Points are awarded for each opposing team member being marked and for the team who captures the oppositions flag.
IntroductionApplication: Paintball games • S.P.L.A.T.’s involvement: • Survey the paintball playing field, acquire the target, range • the target, and shoot the target. (shown in video)
IntroductionDesign Specifications • Working prototype of tank vehicle • Firing mechanism that can achieve a rate of approximately 1 paintball per second • Circuit capable of sensing target and obstacles • Circuit that operates the tank autonomously • Power system will operate for at least 30 minutes • Vehicle emits less than 100 dB of acoustic noise • Complies to FCC standards • Keep cost under 500 dollars
General theory Materials and technologies • Infrared • Ultrasonic • Basic Stamp I and II • Brushless DC motors • Servo motor • DC power relay • Paintball gun
General theory Infrared and Ultrasonic technologies • Utilized in a Sonin combo pro CST • electronic measuring device • Main triggers sends IR signal . Starts timer. • Target responds with ultrasonic signal. target main
General theory Basic stamp modules Basic Stamp I (qty 2) • Control the IR and ultrasonic • technologies • Output is a binary output saying if an object is • a target and what its’ range is • Detects obstacles and has a binary code for that • instance. • Makes sure tank avoids collision with obstacles Basic Stamp II (qty1) • Control servo motor used on paintball gun • Controls the main brushless motors
General theory Flow Diagrams (targeting stamp)
General theory Flow Diagrams (brains)
General theory Flow Diagrams(obstacle avoidance)
General theory Circuit connection
Performance Test Specification: Modularity Test • Firing Mechanism: Basic stamp given a logic high which actuates the servo motor hooked onto the trigger. This test purpose is to see if gun shoots in spec. • Physical tank vehicle maneuverability: Test the tank in different playing condition environments. This includes wet/dry grass and dirt • Obstacle Avoidance: An object will be placed in front of the vehicle while it is operating. • IFF: The transmitter and target will be placed at different distances. The output of the stamp will be logged at the varying distances.
Performance Test Certification: Modularity Test • Firing Mechanism: There was 95% success rate on this test. Failures were due to slippage of servo. • Physical tank vehicle maneuverability: The tank most accurately maneuvered in dry grass or hard surfaces. The least favorable conditions were on wet grass. • Obstacle Avoidance: There was a 100% rate of success when there was an obstacle in the way. • IFF: The sensor setup had an 80% success rate of telling if the target was in line with the transmitter. This is attributed to the fact that the IR signal is directional and missed the target.
Performance Test Specification: A.I. Algorithm • Over 60 foot range: Target will be placed at 75-150 feet ranges. The stamp should receive the signal and send a signal to actuate motors. • 60-20 foot range: Target will be placed within a range of 60-20 feet. The stamp should receive the signal and send a signal to actuate motors in a forward direction and firing mechanism. • Under 20 foot range: Target will be placed within 20 feet. The stamp should receive the signal and send a signal to actuate motors in a reverse direction.
Performance Test Certification: A.I. Algorithm • Over 60 foot range: The sensors proved to be 70% percent accurate. • 60-20 foot range: The sensors proved to be 78% percent accurate. • Under 20 foot range: The sensors proved to be 88% percent accurate. • * The lower percent rates for the longer distances are attributed to the long distance and the narrow beam width of the IR sensor.
Performance Test Specification: Device Test Evaluation • This test will involve the qualification of the real time and accuracy of the system. All algorithms will be combined in paintball style environment. • The power consumption will be evaluated.
Performance Test Certification: Device Test Evaluation • The device test was the culmination of all the different aspects. • This test proved that the tank did not track the target consistently. The tank would start to track the target, but would fall off quickly. This is attributed to the device not receiving the proper signals all the time. • If the target was within the 20 to 60 foot range the tank would fire a round 60% of the time and hit the target 20% of the time. The hit percentage would have been higher if target tracking would have been more consistent.
Performance Test Specification: Class B Emissions • This test will be conducted with the use of a hand held radio transmitter inside the tank this is consistently transmitting. The transmission will be listened to on a similar radio for static and other distortions of the signal.
Performance Test Certification: Class B Emissions • There was no noticeable noise content introduced into the hand held radio when the tank was operated. The test was a success.
Conclusions • Tank is fully mobile • Gun shooting mechanism is a success • IFF algorithm works in a static arena • Safety features make sure tank is safe • The tracking/IFF algorithm is not a success • Reasons: • circuit not fast enough • ultrasonic is affected by temperature and humidity
Future Direction • Turret changes elevation • Tank body made of a more durable material than wood • Use higher frequency emitters instead of sound waves to pinpoint target. • Install a neural network where tank will learn behaviors and not depend on just emitters but more of a visual network(DSP).