Obstacle Avoidance
This paper investigates the finite-time formation control problem for high-order nonlinear multiagent systems (MASs) with consideration of obstacle avoidance, unmeasurable states and dead-zone input. A neural networks k-filter observer is designed to estimate the unmeasurable states and cope with the problem of dead-zone input. Also, by using a tangent type Lyapunov barrier function (LBF), the obstacle avoidance mission can be completed for MASs without dynamic mismatching.
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Obstacle avoidance methodologies seldom work under assumption or limitation of other obstacel implying similar collision averting protocol. Furthermore, prerequisite of having perfect sensing and having central communciation are also needed in order to safely navigate witout collision. A novel obstacle avoidance method of FLC-ORCA is initiated in an attemp to fill in the void within obstacle avoidance. The proposed method is compared with other state-of-the-art tecniques such as Improved A-Star and Directional ORCA.
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