This paper proposes a novel integrated dynamic method based on Behavior Trees for planning and allocating tasks in mixed human robot teams, suitable for manufacturing environments. The Behavior Tree formulation allows encoding a single job as a compound of different tasks with temporal and logic constraints. In this way, instead of the well-studied offline centralized optimization problem, the role allocation problem is solved with multiple simplified online optimization sub-problem, without complex and cross-schedule task dependencies. These sub-problems are defined as Mixed-Integer Linear Programs, that, according to the worker-actions related costs and the workers' availability, allocate the yet-to-execute tasks among the available workers. To characterize the behavior of the developed method, we opted to perform different simulation experiments in which the results of the action-worker allocation and computational complexity are evaluated. The obtained results, due to the nature of the algorithm and to the possibility of simulating the agents' behavior, should describe well also how the algorithm performs in real experiments.

In this work, we propose a novel visuo-haptic guidance interface to enable mobile collaborative robots to follow human instructions in a way understandable by nonexperts. The interface is composed of a haptic admittance module and a human visual tracking module. The haptic guidance enables an individual to guide the robot end-effector in the workspace to reach and grasp arbitrary items. The visual interface, on the other hand, uses a real-time human tracking system and enables autonomous and continuous navigation of the mobile robot towards the human, with the ability to avoid static and dynamic obstacles along its path. To ensure a safer human-robot interaction, the visual tracking goal is set outside of a certain area around the human body, entering which will switch robot behaviour to the haptic mode. The execution of the two modes is achieved by two different controllers, the mobile base admittance controller for the haptic guidance and the robot's whole-body impedance controller, that enables physically coupled and controllable locomotion and manipulation. The proposed interface is validated experimentally, where a human-guided robot performs the loading and transportation of a heavy object in a cluttered workspace, illustrating the potential of the proposed Follow-Me interface in removing the external loading from the human body in this type of repetitive industrial tasks.

This paper proposes a novel loco-manipulation control framework for the execution of complex tasks with kinodynamic constraints using mobile manipulators. As a representative example, we consider the handling and re-positioning of pallet jacks (or lifts/carriers with similar characteristics) in unstructured environments. This task is associated with significant challenges in terms of locomotion, due to the mobility constraints that are imposed by their limited kinematics while moving, and manipulation, due to the existence of dynamic uncertainties while grasping and handling of pallet jacks. To tackle these challenges, our solution enables the robotic platform to autonomously reach a pallet jack location while avoiding the obstacles, and to detect and manipulate its handle by fusing the perception and the contact force data. Subsequently, the transportation of the pallet jack is achieved through a whole-body impedance controller and a trajectory planner which takes into account the mobility constraints of the robot-pallet jack chain. We demonstrate the effectiveness of the proposed solution in reaching and displacing the pallets to desired locations through simulation studies and experimental results. While these results reveal with a proof-of-concept the effectiveness of the proposed framework, they also demonstrate the high potential of mobile manipulators for relieving human workers from such repetitive and labor intensive tasks. We believe that this extended functionality can contribute to increasing the usability of mobile manipulators in different application scenarios.