Shared control approaches for robotic wheelchairs aim to provide navigation assistance to humans by utilizing robot’s intelligence in environment perception and motion planning. They can be broadly classified into two categories based on human intention prediction. Without human intention prediction, control authority lies with humans and assistance is provided only to avoid collisions. This can cause difficulty in cases where fine motor control is required, such as when entering narrow doorways, especially for users with severe upper limb disability. Intention prediction based approaches are able to better assist with such tasks but do not give enough control authority to the user as possible user intentions are pre-defined. In this work, we present an intention prediction based shared control system for point-to-point navigation of wheelchair which gives control authority to the user and also assists in fine motor control tasks. We compute various possible user intentions online using generalized Voronoi diagram, link them across time steps using their homotopy class and thus are able to calculate their probability given user input history. A shared local path planner then steers user towards the most likely path. This allows the user to follow any path. Our simulation experiments with 18 healthy subjects and both simulation and real wheelchair experiments, with 2 Cerebral Palsy (CP) subjects, show that our system can improve navigation outcome for people with disability and in general leads to around 10% faster completion of the task for even healthy people as compared to a local obstacle avoidance system while allowing users to follow their desired path with similar accuracy.

The lack of intuitive and active human–robot interaction makes it difficult to use upper-limb-assistive devices. In this paper, we propose a novel learning-based controller that intuitively uses onset motion to predict the desired end-point position for an assistive robot. A multi-modal sensing system comprising inertial measurement units (IMUs), electromyographic (EMG) sensors, and mechanomyography (MMG) sensors was implemented. This system was used to acquire kinematic and physiological signals during reaching and placing tasks performed by five healthy subjects. The onset motion data of each motion trial were extracted to input into traditional regression models and deep learning models for training and testing. The models can predict the position of the hand in planar space, which is the reference position for low-level position controllers. The results show that using IMU sensor with the proposed prediction model is sufficient for motion intention detection, which can provide almost the same prediction performance compared with adding EMG or MMG. Additionally, recurrent neural network (RNN)-based models can predict target positions over a short onset time window for reaching motions and are suitable for predicting targets over a longer horizon for placing tasks. This study’s detailed analysis can improve the usability of the assistive/rehabilitation robots.

In shared autonomy (SA), accurate user intent prediction is crucial for good robot assistance and avoiding user–robot conflicts. Prior works have relied on passive observation of joystick inputs to predict user intent, which works when the goals are clearly separated or when a common policy exists for multiple goals. However, they may not work well when grasping objects to perform daily activities, as there are multiple ways to grasp the same object. We demonstrate the need for active information-gathering in such cases and show how this can be done in a principled manner by formulating SA as a discrete action partially observable Markov decision process (POMDP), reasoning over high-level actions. One of our insights is that apart from having explicit information-gathering actions and goal-oriented actions, it is important to have actions that move toward a distribution of goals and provide no assistance in the POMDP action space. Compared with a method with no active information-gathering, our method performs tasks faster, requires less user input, and decreases opposing actions, especially for more complex objects, getting higher ratings and preference in our user study.