On-Line Obstacle Detection, Avoidance, and Mapping of an Outdoor Quadrotor Using EKF-Based Fuzzy Tracking Incremental Control

Abstract

The unknown two-dimensional obstacles, including different shapes, numbers, locations, and in the vicinity of the planning path for an outdoor quadrotor, are on-line detected by a 360-degree Li-Dar Sweep Scanse. Based on the comparison among detected triangles containing extreme points of each obstacle, the central orientation of the triangle with the maximum detected distance and the angle larger than the specific threshold is assigned to fly a specific distance for obstacle avoidance. Nevertheless, the quadrotor probably encounters a dead end and is then navigated to a specific altitude for the continuous procedure of obstacle detection. By on-line detection at different waypoints for the same obstacle, an obstacle mapping is also established by all the obstacle detections. Since the sensing signals of an outdoor quadrotor are easily affected by stochastic noise and the quadrotor is operated in ''x'' configuration, an extended Kalman filter based fuzzy tracking incremental control (EKF-FTIC) is employed to simultaneously execute the path tracking, obstacle avoidance, and target approach. Finally, the experiments with 5 unknown obstacles in the neighborhood of a planning path connecting start point with end point validate the effectiveness and feasibility of the proposed method. INDEX TERMS Extended Kalman filter, fuzzy tracking incremental control, on-line obstacle detection, avoidance and mapping, quadrotor.

Recurrent Neural Network With Fractional Learning-Based Fixed-Time Formation Tracking Constrained Control for a Group of Quadrotors

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“…This broad interest in KF is due to its optimality, convenient form for online real-time processing, easy formulation and implementation (Serra 2018;Schimmack et al 2018;Huang et al 2018). The increasing in the complexity of practical dynamic systems has motivated researches in the sense of extending Kalman's filtering theory to face with nonlinearities and uncertainties, by using fuzzy systems theory, mainly the type-2 fuzzy systems, for applications in the area of modeling and control (Gil et al 2019;Bouhentala et al 2019;Hwang et al 2019;Evangelista and Serra 2020;Serra 2018). These recent successful applications of type-2 fuzzy system are due to its structure based on rules, its capability of approximate functions, where the antecedent propositions describe fuzzy operation regions (uncertainties) and the consequent propositions express a nonlinear mapping of the physical behavior inherited to corresponding fuzzy operation regions (Zhang et al 2020;Mendel 2019;Liang and Mendel 2000).…”