Developing mobile robot obstacle avoidance methods with model-based and learning-based methods

Abstract

Mobile robot navigation is a crucial area of research and development in robotics that focuses on enabling robots to move autonomously in their environments. Mobile robots are increasingly being used in a wide range of applications, including manufacturing, healthcare, transportation, and search and rescue missions. These robots have the potential to improve efficiency, reduce costs, and enhance safety in a variety of industries. However, for mobile robots to be effective, they must be able to navigate their surroundings with accuracy and reliability. Navigation involves the robot's ability to perceive its environment, plan a path, and execute that path while avoiding obstacles and other hazards. The development of mobile robot navigation systems has been a major area of focus in robotics research for several decades, and it continues to evolve rapidly. Advances in technologies such as sensors, computing, and machine learning have enabled mobile robots to navigate more complex environments and perform increasingly sophisticated tasks. As such, mobile robot navigation is a critical area of study for researchers and engineers who seek to develop intelligent and autonomous systems that can operate in real-world environments. Path planning and obstacle avoidance are two important topics in robotics that are closely related. Path planning refers to the process of determining a safe and efficient path for a robot to travel from its current location to a desired destination. This process takes into account the robot's movement capabilities, the environment it is operating in, and any obstacles that may be present. Obstacle avoidance, on the other hand, involves the robot's ability to detect and avoid obstacles as it navigates its environment. This is an essential component of path planning, as the robot must be able to react to changes in its environment and modify its path accordingly in order to avoid collisions and ensure safety. Both path planning and obstacle avoidance are critical for the development of autonomous robots that can navigate complex environments and perform tasks without human intervention. These topics are the focus of ongoing research in the field of robotics, and advances in technologies such as sensors, mapping algorithms, and machine learning are enabling robots to navigate increasingly complex environments with greater efficiency and safety. This study proposes three novel contributions in the field of robotics. The first is a novel model-based obstacle avoidance method that plans local trajectories by passing through gaps between obstacles. The second is a learning-based sampling method that improves the efficiency of trajectory planning for path planning algorithms. Finally, we proposed a non-holonomic local planner that uses a CNN-based sampling technique. These contributions aim to improve the navigation and path planning capabilities of robots, allowing them to operate more efficiently and safely in complex environments. Overall, this thesis demonstrates the potential of using advanced techniques and technologies, such as machine learning and local planning, to enhance the performance and capabilities of mobile robots.