A Study on the UWB-Based Position Estimation Method Using Dead Reckoning Information for Active Driving in a Mapless Environment of Intelligent Wheelchairs

Abstract

As the world enters an aging and super-aged society, the application of advanced technology in assistive devices to support the daily life of the elderly is becoming a hot issue. Among them, electric wheelchairs are representative assistive devices for the walking support of the elderly, and their structural form is similar to AGV and AMR. For this reason, research is being introduced and underway to guarantee the right to voluntarily move or improve the convenience of movement for the elderly and severely disabled people who have difficulties in operating a joystick for operating an electric wheelchair. Autonomous driving of mobile robots is a technology that configures prior information on the driving environment as a map DB and operates based on it. However, active driving assistance technology is needed because wheelchairs do not move in a limited space, but can move to a place without a prior map DB or vehicle boarding depending on the passenger’s intention to move. Therefore, a system for estimating the moving position and direction of the wheelchair is needed to develop a driving assistance technology in the relevant driving environment. In order to solve the above problem, this study proposes a position and direction estimation algorithm suitable for active driving of a wheelchair based on a UWB sensor. This proposal is an algorithm for estimating the position and direction of the wheelchair through the fusion of UWB, IMU, and encoder sensors. In this proposal, it is difficult to design an active driving assistance system for wheelchairs due to low accuracy, obstacles, and errors due to signal strength in the position and direction estimation with UWB sensors alone. Therefore, this study proposes a wheelchair driving position and direction estimation system that fuses the dead recording information of a wheelchair and the UWB-based position estimation technique based on sensors applied in IMU and encoders. Applying quantitative verification to the proposed technique, the direction estimation accuracy of the wheelchair of about 15.3° and the position estimation error average of ±15 cm were confirmed, and it was verified that a driving guide for active driving was possible when the sensor system proposed in a mapless environment of the wheelchair was installed at a specific destination.

Three-Dimensional Dead-Reckoning Based on Lie Theory for Overcoming Approximation Errors

“…The measured input U t k is the same as Equation ( 6) in Section 2. The expression exp(U t k ˆ• △t) is calculated from Equation (15). The comprehensive overview of the entire algorithm is presented as a block diagram, as shown in Figure 2.…”

“…Research aimed at minimizing cumulative errors to enhance the accuracy of DR is actively being conducted across various fields [14][15][16][17][18]. Shurin et al (2022) proposed pedestrian DR to address the drift issue over time caused by errors and noise in inertial sensor measurements in the navigation of quadcopters [14].…”

“…Shurin et al (2022) proposed pedestrian DR to address the drift issue over time caused by errors and noise in inertial sensor measurements in the navigation of quadcopters [14]. Jang et al (2024) developed a position and direction estimation algorithm for active driving of wheelchairs using ultrawideband (UWB) sensors [15]. Zhang et al (2023) proposed an accurate underwater DR mathematical model based on the motion model of autonomous underwater vehicles (AUVs) to recursively compute ground truth and associated errors [16].…”