Collision Avoidance in Cluttered Environments: A Low-cost Mechatronic Approach for Autonomous Robots

Wampamba, Alexandria; Hakim-Elahi, Mansour

doi:10.22068/ase.2025.726

Volume 15, Issue 4 (12-2025) ASE 2025, 15(4): 4887-4894 | Back to browse issues page

‎ 10.22068/ase.2025.726

Mendeley

Zotero

RefWorks

Wampamba A, Hakim-Elahi M. Collision Avoidance in Cluttered Environments: A Low-cost Mechatronic Approach for Autonomous Robots. ASE 2025; 15 (4) :4887-4894
URL: http://ase.iust.ac.ir/article-1-726-en.html

Collision Avoidance in Cluttered Environments: A Low-cost Mechatronic Approach for Autonomous Robots

Alexandria Wampamba

, Mansour Hakim-Elahi

Ahlul Bayt International University

Abstract: (253 Views)

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.

Keywords: Collision Avoidance, Dynamic Window Approach (DWA), Spatial Memory, Sensor Fusion, Mechatronic System

Full-Text [PDF 582 kb] (148 Downloads)

Type of Study: Research | Subject: Autonomous vehicles