An optimization-based shared control framework with applications in multi-robot systems

In recent years, researchers have been developing tools to allow human operators to work with multiple robots. To this end, results for autonomous systems can be helpful, e.g. controllability analysis [1], null-space approach [2], containment control [3]. On the other hand, results from teleoperation systems show that the shared control method is helpful in producing safe and efficient humanrobot-interaction systems [4]. Among existing studies on shared control [5, 6], most approaches can be seen as variants under the policy blending framework (1),