A review of computer vision for semi-autonomous control of assistive robotic manipulators (ARMs)

Abstract

Purpose: The advances in artificial intelligence have started to reach a level where autonomous systems are becoming increasingly popular as a way to aid people in their everyday life. Such intelligent systems may especially be beneficially for people struggling to complete common everyday tasks, such as individuals with movement-related disabilities. The focus of this paper is hence to review recent work in using computer vision for semi-autonomous control of assistive robotic manipulators (ARMs). Methods: Four databases were searched using a block search, yielding 257 papers which were reduced to 14 papers after applying various filtering criteria. Each paper was reviewed with focus on the hardware used, the autonomous behaviour achieved using computer vision and the scheme for semi-autonomous control of the system. Each of the reviewed systems were also sought characterized by grading their level of autonomy on a pre-defined scale.Conclusions: A re-occurring issue in the reviewed systems was the inability to handle arbitrary objects. This makes the systems unlikely to perform well outside a controlled environment, such as a lab. This issue could be addressed by having the systems recognize good grasping points or primitive shapes instead of specific pre-defined objects. Most of the reviewed systems did also use a rather simple strategy for the semi-autonomous control, where they switch either between full manual control or full automatic control. An alternative could be a control scheme relying on adaptive blending which could provide a more seamless experience for the user.Implications for rehabilitationAssistive robotic manipulators (ARMs) have the potential to empower individuals with disabilities by enabling them to complete common everyday tasks. This potential can be further enhanced by making the ARM semi-autonomous in order to actively aid the user.The scheme used for the semi-autonomous control of the ARM is crucial as it may be a hindrance if done incorrectly. Especially the ability to customize the semi-autonomous behaviour of the ARM is found to be important.Further research is needed to make the final move from the lab to the homes of the users. Most of the reviewed systems suffer from a rather fixed scheme for the semi-autonomous control and an inability to handle arbitrary objects.

Keywords: ARM; Computer vision; assistive robotic manipulators; exoskeleton; machine learning; robotics; semi-autonomous control; shared control.