18th International Conference on Innovative Computing, Information and Control (ICICIC2024)
Onsite (Qinhuangdao, China) and Online, September 10-13, 2024

PS-25: ICICIC2024-154
Obstacle Avoidance Control Method for Wheelchair Robots Considering Operating Habits
and Fatigue State of User
11,11

1

2

Deshuai Zou , Yina Wang *, Yanjun Yu , Guoqiang Fu , Ziteng Wang and Shuoyu Wang
1

School of Electrial Engineering, Shenyang University of Technology, Shenyang 110870, P. R. China
2
School of System Engineering, Kochi University of Technology, Kochi 782-8502, Japan*Corresponding author: wang.yina@sut.edu.cnIntroduction

Mathematical Formulas

Shared control methods for wheelchair robots
aim to balance human intention and environment
perception to accomplish robot motion planning andprovide navigation assistance to humans. However,different users have different individual operating
habits, which have an impact on the shared motion
control of intelligent wheelchair robots. Furthermore,user’s fatigue state also affects the safety of sharedcontrol. In this work, we propose a novel sharedcontrol system that takes into account both operating
habits and physical states, which introduces adynamic reinforcement learning strategy to adapt
itself to the user’s operating habits, and alsoestablishes a mental fatigue detection model tomonitor the user’s fatigue state to further ensure theuser’s safety. Firstly, we utilize a reinforcement
learning strategy to dynamically adjust the user
control weights in the shared control to adapt todifferent individual habits. Then, to further improve
the safety of shared control oriented to user’s fatigue
state, we introduce a pulse wave blood pressure
sensor to detect the user’s brain fatigue. Especially,
we establish a mental fatigue detection model basedon the variation of heart rate to measure the user’sfatigue factor to realize the monitoring of the user’sindividual and fatigue state. Finally, in order toevaluate the performance of the proposed method,
we conducted experiments to verify the effectivenessof the proposed algorithm.

Reinforcement learning strategies are rewarded
based on the following formulas

R0
Rt = R+
 1 R2 + R 3

if collision happens
otherwise=R1 K1 × tanh(d − d s )

(1)
(2)

2−|β c |R2 = K 2 × e 1458
R3 = K 3 × ( β ct − β ct-1 )(4)

β=β a ( w + a) + β h (1 − ( w + a))s(5)f ( x )= sgn  w ⋅ φ ( x ) + b N= sgn  ∑ yi ai K ( xi ⋅ x ) + b  i =1
(6)

The formula for the control wheelchair is(3)

The optimal decision function for the fatigue
detection model is

Methodologies
Dynamic Reinforcement Learning Strategy: To
address the variability in individual operating habits, theresearchers implemented a dynamic reinforcement
learning (RL) strategy within the shared control system.
Mental Fatigue Detection Model: Recognizing thata user’s mental fatigue can significantly impact thesafety and efficacy of shared control, the researchers
introduced a mental fatigue detection model.

Research Questions
How can a shared control system for intelligent
wheelchair robots be designed to account for both theindividual operating habits and physical states (specificallymental fatigue) of users, thereby enhancing the safety andeffectiveness of robot-assisted navigation?

TablesTable 1. SVM classification results
Fatigue level

Number of test samples

Predicted accurate sample size

Accuracy

Normal state
Fatigue state

20020018016890%
84%

18th International Conference on Innovative Computing, Information and Control (ICICIC2024)
Onsite (Qinhuangdao, China) and Online, September 10-13, 2024

FiguresFigure 1. Data communication schematic

Figure 2. System block diagram

Figure 3. Correlated data changes in fatigueConclusion
This paper proposes a human-robot sharedintelligent obstacle avoidance method forwheelchair robots considering operating habits andbody states. The shared control scheme based on
reinforcement learning is sufficient to acquire thecharacteristics of the user’s operating habits andautomatically adapt to the user’s behavior toimprove the user’s comfort. Then, the detecteduser fatigue is applied to the designed state fusion
shared controller, which enables the controller totake into account both operating habits andindividual states, thus further improving the comfort
and safety of user operation. The fatigue detection
model designed in this paper has a recognition
success rate of 87% and can better recognize thefatigue state of the user. In the future, for better
monitoring, we will introduce other sensors toimprove the recognition rate.