Adaptive Deep Reinforcement Learning Hybrid Neuro-Fuzzy Inference System Based Path Planning Algorithm for Mobile Robot

Mobile robot route planning is the process of calculating a mobile robot's collision-free path from a starting place to a goal point surrounded by its environment. It is a critical component of mobile robotics since it enables robots to move around and perform tasks independently in a variety of conditions. The Global Positioning System (GPS), the Adaptive Neuro-Fuzzy Inference System (ANFIS), and deep reinforcement learning (DRL) are commonly used tools for tracking as well as control. This paper proposes a GPS-based DRL-ANFIS navigation method for mobile robots that avoid collisions. The GPS-based controller keeps the robot on track to achieve its global and flexible objective. Next, a fuzzy inference system (FIS) is employed to simulate obstacle avoidance using fuzzy linguistics on distance sensor data. In addition, a mobile robot path planning technique based on enhanced DRL is proposed to address the issues of limited exploration capability and sparse reward of environmental state space in mobile robot route planning in unfamiliar environments. Finally, the proposed ANFIS parameters are fine-tuned using a tent-based artificial hummingbird algorithm (AHA) to attain the desired location. The proposed approach evaluates the results using MATLAB. The simulation study is designed to assess the proposed strategy's effectiveness in navigating a mobile robot across a complex environment, as well as its performance in comparison to existing collision-free navigation systems. As a result, the proposed approach takes a shorter path and avoids barriers to get the robot closer to its destination. The proposed approach has a computation time of 22 s and a path planning efficiency of 96.56%, which is 5.56% higher than the traditional DRL model.