ABSTRACT
The general purpose of a decision matrix and the procedure for creating it are explained, along with the steps involved in its development, highlighting its advantages and disadvantages. Specialized aspects of a decision matrix in autonomous robot artificial intelligence (AI) are dealt with. Common AI algorithms used in conjunction with decision matrices in autonomous robots include Bayesian inference, reinforcement learning, and neural networks. Chief considerations when using decision matrices are complexity management, examination of unforeseen cases, and the explainability of the decision-making process. Key elements of a self-driving vehicle decision matrix, including its decision-making process and challenges involved, are outlined. Other algorithms discussed are the bug algorithm (a simple, reactive obstacle avoidance strategy where a robot follows the edge of an obstacle until it can resume its path toward the goal); the vector field histogram (a real-time motion planning algorithm that utilizes a polar histogram representing the density of obstacles in different directions to identify obstacle-free directions for steering a robot based on sensor data), and the generalized Voronoi diagram algorithm (partitioning a space into regions based on proximity to multiple seed points like landmarks and waypoints for planning its path to find the most efficient route around obstacles). These four algorithms operate with different approaches to obstacle avoidance and path calculation.
