Design, simulation, and optimized path planning of a smart mecanum wheelchair using RRT* algorithm
- Publisher:
- IOP Publishing
- Publication Type:
- Journal Article
- Citation:
- Engineering Research Express, 2025, 7, (4), pp. 1-22
- Issue Date:
- 2025-12-31
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The present study reports the design and assessment of a Mecanum wheel-based intelligent wheelchair that can navigate independently in dynamic interior environments. The system combines encoder-driven odometry, sensor-based perception, and mechanical design into a single control architecture. Static analysis in ANSYS is used to verify the wheelchair’s structural integrity after it has been modelled in SolidWorks to support a 120 kg payload. Omnidirectional mobility is made possible by four Mecanum wheels, which improve manoeuvrability in tight areas. For path design, the quickly exploring Random Tree Star technique is used. It is optimized through node rewiring and B-spline smoothing to produce workable, smooth paths in real time. While quadrature encoders guarantee precise pose estimation with Mecanum-specific inverse kinematics, RGB-D cameras offer dynamic obstacle recognition and ambient mapping. By combining onboard odometry with real-time visual feedback within a closed-loop path planning framework, the current study fills the gap left by previous autonomous wheelchair systems’ poor adaptability and controller dependency. Four more challenging navigation situations are used to evaluate the performance. The system obtained success rates of 100%, 98.4%, 97.6%, and 96.7%, respectively, in cases 1–4, displaying reliable obstacle avoidance and constant convergence. With increased node sampling and collision checks, the computational cost rose with complexity (219 ms to 270 ms). For variable surroundings, the wheelchair runtime differed from 72 min to 48 min, while the path time changed from 32 seconds to 48 seconds. The anticipated path is more closely aligned with the actual trajectory when the Root Mean Square Error value is lower (0.015–0.03). These findings demonstrate the system’s capacity for precise, flexible, and efficient navigation while highlighting the need for more optimization to lower computational load and improve energy economy for real-time assistive mobility.
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