Toward mission-dependent long robotic arm enhancement: design method of flying watch attachment allocation based on thrust drivability

Long robotic arms are useful for many applications such as nuclear plant decommissioning, inspection, and firefighting. A major problem for designing and operating long robotic arms is that even small end effector reaction forces and arm gravity can result in large loads on proximal arm joints because of long moment arms. To solve that problem, previous researches focus on specifically designed long arms with certain compensation mechanisms. However, those specialized arm designs are difficult to be applied to existing long robotic arms and to be customized for different missions. To overcome those two drawbacks, we recently proposed a watch-like thrust-generating modular device, called flying watch, with the following two major advantages. Firstly, flying watch can be attached to different kinds of existing long robotic arms and generate thrusts to enhance arm strength. And we have proposed a thrust planning method for flying watch in our previous work. Secondly, since different flying watch attachment allocations can enhance the same robotic arm in different ways, flying watch attachment allocations can be customized to meet the needs of a specific mission. However, up to now, customizing flying watch attachment allocations to different missions is still based on human experience and there is no clear performance metric and automated design method for flying watch attachment allocation. To facilitate mission-dependent long arm enhancement, in this paper, we first propose a novel performance metric, called thrust drivability, which measures the ability of a flying watch attachment allocation to counteract unexpected end effector reaction forces. Then based on thrust drivability, we propose an automated design method, called Allocation Optimization based on Weighted Situations (AOWS), for generating mission-dependent flying watch attachment allocations counteracting both unexpected and known external forces. Simulations show that AOWS based allocation designs can counteract both known and unexpected external forces much better than human-experience-based allocation designs.