The Classification and New Trends of Shared Control Strategies in Telerobotic Systems: A Survey

Shared control, which permits a human operator and an autonomous controller to share the control of a telerobotic system, can reduce the operator's workload and/or improve performances during the execution of tasks. Due to the great benefits of combining the human intelligence with the higher power/precision abilities of robots, the shared control architecture occupies a wide spectrum among telerobotic systems. Although various shared control strategies have been proposed, a systematic overview to tease out the relation among different strategies is still absent. This survey, therefore, aims to provide a big picture for existing shared control strategies. To achieve this, we propose a categorization method and classify the shared control strategies into 3 categories: Semi-Autonomous control (SAC), State-Guidance Shared Control (SGSC), and State-Fusion Shared Control (SFSC), according to the different sharing ways between human operators and autonomous controllers. The typical scenarios in using each category are listed and the advantages/disadvantages and open issues of each category are discussed. Then, based on the overview of the existing strategies, new trends in shared control strategies, including the autonomy from learning and the autonomy-levels adaptation, are summarized and discussed.

Automated summary

Shared control allows a human operator and an autonomous controller to jointly control a telerobotic system, reducing workload and improving task performance. Despite the various proposed shared control strategies, a systematic overview of their relationships is lacking. This survey categorizes shared control strategies into three categories: Semi-Autonomous control (SAC), State-Guidance Shared Control (SGSC), and State-Fusion Shared Control (SFSC), based on different ways of sharing between human operators and autonomous controllers. The advantages, disadvantages, and open issues of each category are discussed, along with emerging trends such as autonomy through learning and autonomy-level adaptation.