Showing 2 results for Collision Avoidance
Mr Seyed Amir Mohammad Managheb, Mr Hamid Rahmanei, Dr Ali Ghaffari,
Volume 14, Issue 1 (3-2024)
Abstract
The turn-around task is one of the challenging maneuvers in automated driving which requires intricate decision making, planning and control, concomitantly. During automatic turn-around maneuver, the path curvature is too large which makes the constraints of the system severely restrain the path tracking performance. This paper highlights the path planning and control design for single and multi-point turn of autonomous vehicles. The preliminaries of the turn-around task including environment, vehicle modeling, and equipment are described. Then, a predictive approach is proposed for planning and control of the vehicle. In this approach, by taking the observation of the road and vehicle conditions into account and considering the actuator constraints in cost function, a decision is made regarding the minimum number of steering to execute turn-around. The constraints are imposed on the speed, steering angle, and their rates. Moreover, the collision avoidance with road boundaries is developed based on the GJK algorithm. According to the simulation results, the proposed system adopts the minimum number of appropriate steering commands while incorporating the constraints of the actuators and avoiding collisions. The findings demonstrate the good performance of the proposed approach in both path design and tracking for single- and multi-point turns.
Ms Alexandria Wampamba, Dr Mansour Hakim-Elahi,
Volume 15, Issue 4 (12-2025)
Abstract
The deployment of autonomous robots in unstructured, cluttered environments remains a significant challenge, particularly for low-cost platforms. While the Dynamic Window Approach (DWA) provides a robust foundation for reactive navigation, its performance is often suboptimal due to a lack of historical context, leading to oscillatory behavior and entrapment in local minima. This paper presents a novel, cost-effective mechatronic system that enhances DWA with a real-time spatial memory module and optimizes its performance using a Bayesian Optimization strategy. Our platform integrates a Raspberry Pi 4 with a fused ultrasonic and infrared sensor suite. The core innovation is a Local Occupancy History Map that provides a short-term, decaying memory of obstacle locations. This memory influences the DWA’s trajectory evaluation, discouraging paths through recently occupied space. Furthermore, we employ Bayesian Optimization loop to automatically tune the critical hyperparameters of the navigation system—the memory decay rate and the history weight—to maximize efficiency and safety. We validate our system in complex indoor environments, comparing the baseline DWA, the DWA with Spatial Memory (DWA-SM), and the optimized DWA-SM (DWA-SM-Opt). Quantitative results demonstrate that the optimized system (DWA-SM-Opt) achieves a 40% reduction in average path completion time and a 65% decrease in collisions compared to the baseline DWA. Qualitative analysis confirms more intelligent, fluid navigation and a consistent ability to escape trapping configurations. This work establishes that the fusion of a lightweight spatial memory with an AI-driven optimization routine, implemented on low-cost hardware, can yield a level of performance previously associated with more complex and expensive systems.
