Mapping, localization and navigation for an assistive mobile robot in a robot-inclusive space

Abstract

Over the years, the major advancements in the field of robotics have been enjoyed more by the mainstream population, e.g. in industrial and office settings, than by special groups of people such as the elderly or persons with impairments. Despite the advancement in various technological aspects such as artificial intelligence, robot mechanics, and sensors, domestic service robots are still far away from achieving autonomous functioning. One of the main reasons for this is the complex nature of the environment and the dynamic nature of the people living inside it. In our laboratory, we have started to address this issue with our minimal degrees of freedom MARIS robot, by upgrading it from a teleoperated robot to an autonomous robot that can operate in a robot-inclusive space that is purposely designed to adopt algorithms that are not very computationally intensive, and hardware architecture that is relatively simple. This paper discusses the implementation of suitable SLAM algorithms, to select the best method for mapping and localization of the MARIS robot in this robot-inclusive environment. The emphasis is on the development of low-complexity algorithms that can map the environment with lesser errors. The paper also discusses the 3D mapping, and the ROS based navigation stack implemented on the MARIS robot, using just a LiDAR, a Raspberry Pi processor, and DC motors with encoders as main hardware architecture, so as to keep low costs.