The mobile manipulator’s base positioning is strategic to optimize the execution of different tasks within a specific workspace. Base placements provided by the human intuition yield suboptimal results, and existing methods are usually based on computationally expensive maps built to represent the distribution of the manipulator’s dexterity or the base positions effective for reaching a specific target, but not both simultaneously. This paper proposes a novel optimization-based method for mobile manipulator’s base placement, focused on a compact mathematical model of the manipulator reachability space. By representing it through two concentric ellipsoid equations, such a model enables efficient evaluation of end-effector reachability while guiding the base placement. This model is exploited in the optimization problem to autonomously reposition the mobile base, ensuring pose reachability with high dexterity and minimizing collision risk. The approach is validated experimentally using a mobile manipulator in a real-world laboratory and assigning target poses with varying difficulty levels. The results demonstrate the effectiveness of the obtained base poses and the adaptability of the method to different scenarios.

Nowadays, mobile manipulators can support humans in daily life tasks while sharing the same workspace. These robots are usually requested to perform pick-and-place actions that involve objects that must be handled with care, since they may hurt the human operators. To optimize their utility as smart assistants, they require autonomous grasping pose generation, object recognition, and pose estimation capabilities. In addition, since they work in dynamic environments, adaptability is essential, hence predefined starting positions for grasping actions should be avoided. These demands are even more challenging for robots with limited computational capabilities. In this paper, we propose an approach that demonstrates how to improve the capabilities of a low-resource mobile manipulator. First, an easy way to model the robot as a unique system for holistic motion planning is developed. Then, we propose a lightweight approach to generate the grasping point to pick a requested item that relies only on the available CPU. Finally, a simple yet flexible solution that involves human feedback is adopted to let the robot handle potentially dangerous objects, while ensuring the operator's safety. The proposed solution has been developed in ROS1 and experimentally tested on the LoCoBot mobile manipulator in a laboratory environment.

A crucial aspect of human-robot collaboration involves the robot’s ability to safely perform handovers of objects to be used by the operator. In this study, we introduce two complementary frameworks designed to execute every stage of a robot-to-human handover process, using only RGB-D input data. The first framework employs a machine learning model, trained on a custom real-world dataset, to detect objects and their parts’ affordances. Affordance is encoded using a novel representation based on keypoints, which are utilized to model hazardous sections of objects and plan appropriate grasps. The second framework tracks hand movements to dynamically determine handover locations, while enforcing safety protocols to prevent exposure of dangerous object parts during the movement. Additionally, it ensures correct object orientation upon delivery, presenting the object handle to the human. The effectiveness of the proposed solution has been successfully validated through testing on a real mobile manipulator.

Mobile manipulators can significantly contribute to enhance the flexibility of several processes, such as automated order-picking systems and various logistic applications, thanks to their capability to manipulate objects and deliver them to different locations. A primary role is envisaged for them in Smart Factories, as workmates of the operators, if they are able to safely navigate in human-shared environments. This paper proposes a lightweight and flexible ROS1-based software architecture, designed for low-resource mobile manipulators, to make them able to autonomously search for the items requested by a human operator, independently from the starting pose, and pick and place them in a predefined depot location. The validity of the proposed architecture, which is potentially applicable to different low-resource mobile manipulators, is proven through its experimental implementation on a Locobot mobile robot.

Mobile robots can highly contribute to achieve the production flexibility envisaged by the Industry 4.0 paradigm, provided that they show an adequate level of autonomy to operate in a typical industrial environment, in which the presence of both static and dynamic obstacles must be managed. Robot Operating System (ROS) is a well known open-source platform for the development of robotic applications, recently updated to the enhanced ROS2 version, including a navigation stack (Nav2) providing most, but not all the capabilities required to a mobile robot operating in an industrial environment. In particular, it does not embed a strategy for dynamic obstacle handling. Aim of this paper is to enhance Nav2 through the development of a Dynamic Obstacle Layer, as a plug and play solution suitable for the integration of the dynamic obstacle information acquired by a generic 2D LiDAR sensor. The effectiveness of the proposed solution is validated through a campaign of simulation tests, carried out in Webots for a TurtleBot3 burger robot, equipped with a RPLIDAR A3 LiDAR sensor.

The brand new paradigm of Industry 5.0 envisages an increased leading role of the human operator in the production lines of the next future. Human-centric oriented solutions are going to be developed based on proactive human-robot collaborations, able to better exploit the skills and capabilities of both humans and cobots, mainly thanks to artificial intelligence. Several functionalities must be assured to reach such a goal, guaranteeing safety and flexibility, from human action prediction to object recognition and affordance. This paper offers an overview of the existing solutions for the various, separate issues, proposing a general framework for mobile manipulators assisting human workers, in a context of mass customization.

Human-robot collaborative applications are generally based on some kind of co-working of the human operator and the robot in the execution of a given task. A disruptive change in the collaborative modalities would be given by the capability of the robot to anticipate how it could be of help for the operator. In case of an Autonomous Mobile Robot (AMR), this would imply not only a safe navigation in presence of a human operator, but the automatic adaptation of its motion to the specific operation carried out by the operator. This paper investigates the possibility of achieving operation recognition by monitoring the human motion on a 2D map and classifying his/her path on the map, taken as an image data sample. Deep learning state-of-the-art libraries and architectures are exploited with the aim of making the robotic system aware of the ongoing process. The reported results, relative to a small training dataset, are nonetheless promising.

In the last decades, industrial manipulators have been used to speed up the production process and also to perform tasks that may put humans at risk. Typical interfaces employed to teleoperate the robot are not so intuitive to use. In fact, it takes longer to learn and properly control a robot whose interface is not easy to use, and it may also increase the operator's stress and mental workload. In this paper, a touchscreen interface for supervised assembly tasks is proposed, using an LCD screen and a hand-tracking sensor. The aim is to provide an intuitive remote controlled system that enables a flexible execution of assembly tasks: high level decisions are entrusted to the human operator while the robot executes pick-and-place operations. A demonstrative industrial case study showcases the system potentiality: it was first tested in simulation, and then experimentally validated using a real robot, in a laboratory environment.

In the near future, an increasing number of mobile agents working closely with human operators is envisaged in smart factories. In industrial human-shared environments that employ traditional Automated Guided Vehicles, safety can be ensured thanks to the support provided by Autonomous Mobile Robots, acting as a net of meta-sensors. The localization and perception information of each meta-sensor is shared among all mobile platforms. In particular, the information about the dynamic detection of human presence is combined and uploaded in a shared map, increasing the awareness of the mobile robots about their surroundings in a specific working area. This paper proposes an architecture that integrates the meta-sensors with an existing net of Automated Guided Vehicles, with the aim of enhancing systems based on outdated mobile agents that seek for Industry 4.0 solutions without the necessity of a complete renewal. Simulations of test scenarios are provided in order to confirm the validity of the proposed architecture model.

In smart factories, the performance of the production lines is improved thanks to the wide application of mobile robots. In workspaces where human operators and mobile robots coexist, safety is a fundamental factor to be considered. In this context, the motion planning of Autonomous Mobile Robots is a challenging task, since it must take into account the human factor. In this paper, an implementation of a three-level online path planning is proposed, in which a set of waypoints belonging to a safe path is computed by a supervisory planner. Depending on the nature of the detected obstacles during the robot motion, the re-computation of the safe path may be enabled, after the collision avoidance action provided by the local planner is initiated. Particular attention is devoted to the detection and avoidance of human operators. The supervisory planner is triggered as the detected human gets sufficiently close to the mobile robot, allowing it to follow a new safe virtual path while conservatively circumnavigating the operator. The proposed algorithm has been experimentally validated in a laboratory environment emulating industrial scenarios.

The presence of mobile agents in the industrial environment is growing, introducing specific safety issues in their path planning. This paper proposes the implementation of a three-level path planning procedure, which allows: (i) the imposition of a set of waypoints, tending to a safe path, generated by a supervisory planner on the basis of a static map of the environment (not necessarily fully updated), (ii) the generation of a global path including such waypoints exploiting a cost-based algorithm, taking into account also the obstacles not included in the static map, but detected at the beginning of the global planning phase, and (iii) the avoidance of dynamic obstacles appearing during the robot motion, thanks to the action of a local planner. The procedure has been experimentally tested to plan the motion of a differential mobile robot.

A growing presence of mobile agents is envisaged in the smart factories scenario of the next future. The safe motion of traditional Automated Guided Vehicles in human-shared workspaces can be achieved thanks to the support of a fleet of Autonomous Mobile Robots, acting as a net of meta-sensors, able to detect the human presence and share the information. This paper proposes a preliminary working implementation of one meta-sensor module, exploiting the synergistic use of different sensors through an overall affordable and accessible sensor data fusion algorithm. Experimental results in a laboratory environment confirm the validity of the approach.