Multi-human multi-robot (MH-MR) systems have the ability to combine the potential advantages of robotic systems with those of having humans in the loop. Robotic systems contribute precision performance and long operation on repetitive tasks without tiring, while humans in the loop improve situational awareness and enhance decision-making abilities. A system's ability to adapt allocated workload to changing conditions and the performance of each individual (human and robot) during the mission is vital to maintaining overall system performance. Previous works from literature including market-based and optimization approaches have attempted to address the task/workload allocation problem with focus on maximizing the system output without regarding individual agent conditions, lacking in real-time processing and have mostly focused exclusively on multi-robot systems. Given the variety of possible combination of teams (autonomous robots and human-operated robots: any number of human operators operating any number of robots at a time) and the operational scale of MH-MR systems, development of a generalized framework of workload allocation has been a particularly challenging task. In this paper, we present such a framework for independent homogeneous missions, capable of adaptively allocating the system workload in relation to health conditions and work performances of human-operated and autonomous robots in real-time. The framework consists of removable modular function blocks ensuring its applicability to different MH-MR scenarios. A new workload transition function block ensures smooth transition without the workload change having adverse effects on individual agents. The effectiveness and scalability of the system's workload adaptability is validated by experiments applying the proposed framework in a MH-MR patrolling scenario with changing human and robot condition, and failing robots.

Numerous types of unmanned surface vehicles (USVs) are currently available for different applications with a wide spectrum of maneuvering capabilities. We present a generalized multi-USV navigation, guidance, and control framework adaptable to specific USV maneuvering response capabilities for dynamic obstacle avoidance. The proposed method integrates offline optimal path planning with a safety distance constrained A* algorithm, and an online extended adaptively weighted (EAW) artificial potential field-based path following approach with dynamic collision avoidance, based on USV maneuvering response times. The framework adaptively weighs inter-USV interaction, waypoint following, and collision avoidance based on USV maneuvering capabilities. The EAW system allows USVs with fast maneuvering abilities to react late and slow USVs to react sooner to oncoming moving obstacles gradually, with a carefully designed series of repulsive potential with diminishing weighting along the predicted path of detected moving obstacles, such that a smooth path is followed by the USV group with reduced cross-track error and reduced maneuvering effort. We emphasize the importance of such requirements in constrained and busy maritime environments such as narrow channels in busy harbors. Simulation results validate the proposed EAW artificial potential field framework for different sized multi-USV teams showing reduced cross-track error and maneuvering effort compared to the unweighted or traditional approach, for both slow- and fast-maneuvering multi-USV teams.

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