HELMI BIN JAMALUDIN

Safe and stable high-speed obstacle avoidance remains a critical challenge for autonomous mobile robots (AMRs) operating on constrained tracks. This paper presents a trajectory planning and sensor fusion framework designed for a 1/12-scale autonomous car robot. To prevent wheel slip and lateral instability during sudden avoidance maneuvers, we propose a trapezoidal steering and velocity profiling algorithm. The steering angle is adjusted through coordinated ramp, hold, and settle phases, while the vehicle decelerates to maintain traction and accelerates back to nominal speed upon maneuver completion. The system incorporates a dual-axle active steering geometry in crab mode, allowing lateral translation with minimal yaw deviation. Perception is achieved by fusing a low-latency ultrasonic sensor with a bird's-eye view camera pipeline. To prevent failure under sensor degradation, an area-based visual distance model is integrated as a real-time fallback. Hardware-in-the-loop experiments conducted on a physical vehicle prototype validate the effectiveness of the proposed architecture. The results show that the trapezoidal trajectory planner achieves a 100% success rate in avoiding obstacles at speed, reducing peak lateral deviation to 3.5 cm and maintaining track stability where baseline static-steering methods fail.

Muhammad Haziq Zuhairy Bin Mohd Hasmadi, HELMI BIN JAMALUDIN, Najmi Hafizi Bin Zabawi, SHAHMIE HAZIQ BIN SAIFUL AZWA Verified by AVIS CertificateFIRA World Summit 2026Submitted 4 Jun 2026