Mini-review: Robotic wheelchair taxonomy and readiness

This research paper presents a novel track based stair-climbing wheelchair design with improved stability and performance. The proposed design has an adaptable arrangement to the staircase especially at the top of the staircase. In the existing wheelchair designs, this particular feature has not been adequately addressed. The proposed design consists of body frame and supporting rubber track arms. Of the two main operations; ascending and descending, the most critical operations is at the top of the staircase, which is smoothly controlled by following the staircase profile. This motion is achieved by properly synchronizing the rubber track arms according to a pre-planned motion profile. As a result, user does not feel sudden motions in position and attitude. Furthermore, the proposed wheelchair is adaptable to any staircase.

Motion Planning of Novel Stair-Climbing Wheelchair for Elderly and Disabled People

The wheelchair is the major means of transport for elderly and physically disabled people in their daily lives. However it cannot overcome architectural barriers such as curbs and stairs. In this study, we developed an inverted-pendulum-type robotic wheelchair for climbing stairs. The wheelchair has a seat slider and two rotary links between the front and rear wheels on each side. When climbing up stairs, the wheelchair rotates the rotary links while maintaining an inverted state of a movable body by controlling the position of the center of gravity using the seat slider. In previous research, we proposed the control method for climbing up stairs using the rotary links and seat slider, confirming a period of approximately 5 s to rotate the rotary links. In this paper, we propose a control method for going down stairs, and experimentally verify the control effectiveness and stability.

Dynamic Stability Control of Inverted-Pendulum-Type Robotic Wheelchair for Going Up and Down Stairs

This paper describes a method of estimating a path to follow from an image in plant-rich environments such as greenhouses and unstructured outdoor scenes. In such environments, there are several factors that make it difficult for robots to determine a path, such as plants covering the path and ambiguous path boundaries. Approaches based on segmentation of traversable regions cannot be applied to such environments because the regions may not be clearly defined or may be occluded. In this work, we propose a method of estimating a path from a single image in the end-to-end fashion. We also develop an automatic annotation method utilizing the robot’s trajectory during the data acquisition phase. We conducted a real-world experiment of robot navigation and confirmed that the proposed method is capable of navigation in paths partially covered by plants. We also confirmed that proposed data annotation method can generate training data more efficiently than manual annotation. 

End-to-End Path Estimation and Automatic Dataset Generation for Robot Navigation in Plant-Rich Environments

We propose a wheeled stilts-type personal micro-mobility. The mobility itself is a form with a wheel on the lower end of a pole-shaped handle, which is used as one mobility in pairs. The user holds the pole-shaped handle on each side in hand and stands on step attached over the wheels with both legs. The two mobilities are completely separated, and the appearance of getting on the mobility is similar to “stilts”. The mobilities move while being inverted pendulum by applying the inverted pendulum control to each left and right body. In this work, we have developed a mobility mechanism prototype with low height-tubular wheels in the drive unit and conduct basic driving experiments. We report the findings and problems from the experiment of flat surface running.

Prototype of Wheeled Stilts-Type Personal Micro-Mobility

The wheelchair is the major means of transport for elderly and physically disabled people in their daily lives. However it cannot overcome architectural barriers such as curbs and stairs. In this study, we developed an inverted-pendulum-type robotic wheelchair for climbing stairs. This wheelchair has a seat slider and two rotary links between the front and rear wheels on each side. When climbing stairs, the wheelchair rotates the rotary links while maintaining an inverted state of a mobile body by controlling the position of the center of gravity using a seat slider. In previous research, we confirmed that the wheelchair can climb by applying the control method consisting of a center-of-gravity control phase and rotary link control phase. However, it took approximately 15 s to rotate the rotary links during climbing because faster climbing causes the movement of wheels and the wheelchair to fall. This paper focuses on a control method to restrain the movement of the wheels when the stair climbing speed is increased. We realized that the movement was caused by forces acting on the pitch angle, such as the inertial force and the reaction of the driving force. We proposed the method considering the dynamic equilibrium of the pitch angle and confirmed the effect of the restraining wheels’ movement when the proposed method was applied.

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Attitude control of an inverted-pendulum-type robotic wheelchair to climb stairs considering dynamic equilibrium

Detection of traversable areas is essential to navigation of autonomous personal mobility systems in unknown pedestrian environments. However, traffic rules may recommend or require driving in specified areas, such as sidewalks, in environments where roadways and sidewalks coexist. Therefore, it is necessary for such autonomous mobility systems to estimate the areas that are mechanically traversable and recommended by traffic rules and to navigate based on this estimation. In this paper, we propose a method for weakly-supervised recommended traversable area segmentation in environments with no edges using automatically labeled images based on paths selected by humans. This approach is based on the idea that a human-selected driving path more accurately reflects both mechanical traversability and human understanding of traffic rules and visual information. In addition, we propose a data augmentation method and a loss weighting method for detecting the appropriate recommended traversable area from a single human-selected path. Evaluation of the results showed that the proposed learning methods are effective for recommended traversable area detection and found that weakly-supervised semantic segmentation using human-selected path information is useful for recommended area detection in environments with no edges.

Weakly-Supervised Recommended Traversable Area Segmentation Using Automatically Labeled Images for Autonomous Driving in Pedestrian Environment with No Edges.

Yuya Onozuka

Papers

Attitude control of an inverted-pendulum-type robotic wheelchair to climb stairs considering dynamic equilibrium

Dynamic Stability Control of Inverted-Pendulum-Type Robotic Wheelchair for Going Up and Down Stairs

Weakly-Supervised Recommended Traversable Area Segmentation Using Automatically Labeled Images for Autonomous Driving in Pedestrian Environment with No Edges.