Reactive Obstacle–Avoidance Systems for Wheeled Mobile Robots Based on Artificial Intelligence

Obstacle–Avoidance robots have become an essential field of study in recent years. This paper analyzes two cases that extend reactive systems focused on obstacle detection and its avoidance. The scenarios explored get data from their environments through sensors generate information for the models based artificial intelligence to obtain a decision. main contribution is discussion aspects allow comparing both approaches, such as heuristic approach implemented, requirements, restrictions, response time, performance. first case presents mobile robot applies fuzzy inference system (FIS) achieve soft turning basing decision depth image information. second introduces multilayer perceptron (MLP) architecture, which class feedforward neural network (ANN), ultrasonic decide how move uncontrolled environment. analysis options offers perspectives choose between or Kinect sensors, infer optimal decisions applying logic networks, with key elements methods design wheels. Therefore, we show AI Fuzzy Logic techniques us learn “experience” by making them safe adjustable new tasks, unlike traditional use large programs perform specific task.