Closest Scuba Diving From Chicago

3.3 Collision risk computation system

The Collision Risk Computation System uses information from the surrounding environment as well as the obstacle and positioning information to compute the risk of the autonomous underwater vehicle colliding with various obstacles that exist in its environment (Kim, 2001; Hara & Hammer, 1993). The system provides a basis for the decisions it makes so that if the system finds the autonomous underwater vehicle at risk of colliding with an obstacle, it changes the navigation path so that it can safely avoid the obstacle.

The Collision Risk Computation System uses fuzzy inference which consists largely of 3 modules as seen in Fig. 10 to compute collision risks the autonomous underwater vehicle might face while navigating in its environment. The first module is the input module that reads in the vector information of the autonomous underwater vehicle and obstacle from the Virtual world, then computes the obstacle's DCPA(Distance of the Closest Point of Approach) and TCPA(Time of the Closest Point of Approach). The second Collision Risk Computation Module then uses fuzzy logic to calculate the risk of collision. It fuzzifies the DCPA and TCPA from the first module and performs a fuzzy-inference, then defuzzifies it to compute the risk of collision. In order to send the computed collision risk value to the Collision Risk Computation System, the third Output Module takes the computed collision risk and transfers it to the Virtual world.

Fig. 10. Collision-risk computation system

The collision risk is computed by fuzzy-inference using DCPA and TCPA as its input. The inference rule uses the centroid method with the min operation as the antecedent and the product operation as the consequent. The membership functions of DCPA and TCPA, which are the input values, and the collision risk, which is the output value, are first defined. Fig. 11, Fig. 12, and Fig. 13 show the membership functions of the DCPA, TCPA and collision risk, respectively. The labels used for each membership function is as follows:

P : Positive, N : Negative, S : Small, M : Medium, B : Big



Fig. 11. Membership function of DCPA(meter)

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