Self-localization is a fundamental capability that mobile robot navigation systems integrate to move from one point to another using a map. Thus, any enhancement in localization accuracy is crucial to perform delicate dexterity tasks. This paper describes a new localization algorithm that maintains several populations of particles using the Monte Carlo Localization (MCL) algorithm, always choosing the best one as the system's output. As novelties, our work includes a multi-scale map-matching algorithm to create new MCL populations and a metric to determine the most reliable. It also contributes the state of the art implementations, enhancing recovery times from erroneous estimates or unknown initial positions. The proposed method is evaluated in ROS2 in a module fully integrated with Nav2 and compared with the current state-of-the-art Adaptive AMCL solution, obtaining good accuracy/recovery times.

Motion Capture systems are crucial in many fields, and Mobile Robotics is one of them. This paper describes an Open Source robotic framework to standardize the use of motion capture systems called MOCAP4ROS2. This framework features a layered architecture that allows building applications that use Motion Capture systems regardless of the specific system model/vendor. The challenges are technical and social: on the one hand, resolving synchronization and representation issues; on the other hand, involving the community to reach a consensus on the necessary interfaces. MOCAP4ROS2 has been implemented in ROS2 and already has drivers (we understand a driver for MOCA4ROS2 as a ROS2 node that publish the MOCAP system information) for today’s main commercial systems.

Facing human activity-aware navigation with a cognitive architecture raises several difficulties integrating the components and orchestrating behaviors and skills to perform social tasks. In a real-world scenario, the navigation system should not only consider individuals like obstacles. It is necessary to offer particular and dynamic people representation to enhance the HRI experience. The robot's behaviors must be modified by humans, directly or indirectly. In this paper, we integrate our human representation framework in a cognitive architecture to allow that people who interact with the robot could modify its behavior, not only with the interaction but also with their culture or the social context. The human representation framework represents and distributes the proxemic zones' information in a standard way, through a cost map. We have evaluated the influence of the decision-making system in human-aware navigation and how a local planner may be decisive in this navigation. The material developed during this research can be found in a public repository (https://github.com/IntelligentRoboticsLabs/social_navigation2_WAF) and instructions to facilitate the reproducibility of the results.