PATH PLANNING AND OBSTACLE AVOIDANCE METHODS FOR AUTONOMOUS MOBILE ROBOTS

Abstract

Navigation and path planning are among the central problems in the development of mobile and autonomous robots. Research in this field has been conducted for decades, and several methodologies have been proposed to solve these problems. In the field, these approaches are divided into classical or deterministic and non-deterministic or heuristic methods. The article provides a brief overview of typical representatives of both classes, as well as an extended review of methods based on artificial potential fields.

Important characteristics of obstacle detection and avoidance algorithms include convergence, computation time, and memory requirements in the system. The need for convergence arises from the requirement to achieve a stable or desired state of the system. This time varies depending on the chosen algorithm, the nature of the task, and the initial conditions. The main goal is to reduce convergence time, i.e., to reach the desired state as quickly as possible. Computation time and memory requirements are important because the robot must respond to the working environment and changes in it in real-time, and autonomous robots usually have quite limited hardware resources. Therefore, these are also important characteristics when selecting a method for a specific task and robot. The modification of the classical artificial potential field method using the Gaussian function to describe repulsive forces is an example of optimizing the method for systems with constrained resources. As of the writing of the article, unmanned aerial vehicles with limited resources are beginning to be widely used, making such optimizations practically valuable.

Among the considered methods, heuristic ones are relatively new and are increasingly finding practical application. Research at the time of writing focuses on optimizing existing algorithms and hybridization to improve efficiency. An example of such hybridization is the artificial potential field method using fuzzy logic. This combines the classical artificial potential field method with a heuristic approach—fuzzy logic. This leads to some complexity in the method but solves typical problems of the classical algorithm, such as local minima, and increases the optimality and smoothness of the path.

Most of obstacle detection and avoidance algorithms are working with only one type of sensor, such as ultrasonic distance sensors, LIDAR, or cameras. Each sensor technology and corresponding algorithms have their advantages and disadvantages. A promising approach is to use several types of sensors and algorithms, combining the results of different algorithms to achieve a more optimal final result, so called sensor fusion. However, it should be noted that this approach will require more sophisticated hardware.

As robots increasingly become part of everyday life, it is quite possible that they will start working in collaboratively and interacting to solve assigned tasks. The development of collaborative methods for obstacle avoidance and interaction between robots in a single working environment is also a promising research direction.

In summary, the gradual robotization of many processes in everyday life or production generates a high demand for research in the field of mobile robotics in general and methods for obstacle detection, avoidance and path planning in particular.

Key words: robotics, obstacle avoidance, path planning, artificial potential field, autonomous robots, mobile robots.