Construction of Novel Self-Adaptive Dynamic Window Approach Combined With Fuzzy Neural Network in Complex Dynamic Environments

Abstract

The traditional Dynamic Window Approach (DWA) with constant weight values of the evaluation function leads to the inability of obstacle avoidance for the Automated Guided Vehicles (AGV) to perform obstacle avoidance and path planning in the complex environment. Effective avoidance of complex obstacles requires adaptive weight adjustment to address the evaluation function's challenges. This paper proposes an adaptive DWA (ADWA), which introduces neural network training on the basis of the Mamdani DWA (MDWA). Firstly, the Mamdani type fuzzy controller is designed, and then the adaptive neuro-fuzzy controller is obtained by neural network training. Then, experiments are carried out through the MATLAB simulation environment. The simulation experiment results show that the improved DWA compared to traditional DWA can make the AGV pass the obstacle environment with a better trajectory and reduce the time. The improved DWA improves the autonomous obstacle avoidance capability of AGVs, which not only perfectly fits our task requirements, but also has apparent scientific and practical significance in developing AGV autonomous obstacle avoidance technology.INDEX TERMS fuzzy control, dynamic window approach, neural network, automatic guided vehicle

An adaptive dynamic window approach for UUV obstacle avoidance planning in 3D environments

“…The traditional DWA uses the evaluation function with constant weight in selecting the best solution, which limits the robot's obstacle avoidance ability in a complex environment. To address this problem, Yang et al [9] used fuzzy neural networks to adjust the weights of evaluation functions to improve the obstacle avoidance ability of robots in complex environments. Aiming at the obstacle avoidance planning problem of robots with uncertainty in system dynamics, Yasuda et al [10] modelled the DWA objective function as a random variable.…”

An Improved Dynamic Window Approach Based on Reinforcement Learning for the Trajectory Planning of Automated Guided Vehicles

“…While plenty approaches focus on utilizing heuristic search algorithms or machine learning strategies to optimize the generated local trajectories by dynamically adjusting the weights of the cost function. Typical methods include fuzzy logic [18,19], particle swarm optimization [20], ant colony optimization [21], imitation learning [22], etc. Above methods require substantial support from expert experiential data or demand a significant amount of computational resources to ensure real-time planning effect, which is not suitable for the environment with complex information.…”