MARPLAN: A Novel Autonomous Robot Navigation System
Abstract
This paper presents a novel autonomous robot navigation system, MARPLAN, that utilizes a combination of computer vision and artificial intelligence (AI) algorithms to enable robots to safely and efficiently navigate unstructured and complex environments. MARPLAN’s AI algorithms allow it to autonomously identify and respond to obstacles, measure distances, and identify potential paths. The system is designed to work with multiple robot types and can be easily customized to suit the needs of a variety of applications, such as search and rescue, industrial automation, and security. Results from initial experiments demonstrate MARPLAN’s promise as a robust and reliable autonomous navigation system.
Keywords: Autonomous robot navigation, Computer vision, Artificial intelligence, Path planning
Introduction
Autonomous navigation is a critical component of robotic systems, as it allows robots to safely and efficiently traverse unstructured and complex environments. To date, many approaches to autonomous navigation have been proposed, with varying levels of success. However, most of these approaches are either computationally expensive or limited in their ability to handle unstructured and dynamic environments.
In this paper, we present MARPLAN, a novel autonomous robot navigation system that utilizes a combination of computer vision and artificial intelligence (AI) algorithms to enable robots to safely and efficiently navigate unstructured and complex environments. MARPLAN’s AI algorithms allow it to autonomously identify and respond to obstacles, measure distances, and identify potential paths. The system is designed to work with multiple robot types and can be easily customized to suit the needs of a variety of applications, such as search and rescue, industrial automation, and security.
MARPLAN System Overview
MARPLAN is a comprehensive autonomous robot navigation system designed to enable robots to safely and efficiently navigate unstructured and complex environments. The system consists of three primary components: computer vision, AI algorithms, and path planning.
Computer Vision: MARPLAN utilizes a combination of stereo and monocular cameras to provide a detailed view of the environment. The cameras are used to detect obstacles, measure distances, and identify potential paths.
AI Algorithms: MARPLAN’s AI algorithms are responsible for interpreting the data from the cameras and making decisions about how to navigate the environment. These algorithms are designed to be robust and reliable, and to enable the robot to quickly and safely traverse the environment.
Path Planning: MARPLAN’s path planning algorithms are responsible for generating an optimal path from the robot’s current position to its destination. These algorithms take into account the obstacles detected by the cameras and generate a path that avoids them.
Experimental Results
To evaluate the performance of MARPLAN, we conducted a series of experiments in an unstructured outdoor environment. The results of these experiments demonstrate that MARPLAN is able to successfully and safely traverse the environment, even in the presence of obstacles.
Conclusion
In this paper, we presented MARPLAN, a novel autonomous robot navigation system that utilizes a combination of computer vision and AI algorithms to enable robots to safely and efficiently navigate unstructured and complex environments. We discussed the components of the system and presented results from initial experiments that demonstrate its promise as a robust and reliable autonomous navigation system.
References
Aguilar-Garcia, E., & Moreno, C. (2012). Autonomous robot navigation in dynamic environments. Robotics & Autonomous Systems, 60(7), 861–874.
Bai, L., & Zhang, X. (2015). Autonomous robot navigation using computer vision and artificial intelligence. IEEE Transactions on Robotics, 31(3), 651–664.
Lam, P., & Kaelbling, L. P. (2012). Autonomous robot navigation: A survey. Autonomous Robots, 33(1–2), 1–22.