A comprehensive review of obstacle avoidance for autonomous agricultural machinery in multi-operational environment

Abstract

As automation becomes increasingly adopted to mitigate labor shortages and boost productivity, autonomous technologies such as tractors, drones, and robotic devices are being utilized for various tasks that include plowing, seeding, irrigation, fertilization, and harvesting. Successfully navigating these changing agricultural landscapes necessitates advanced sensing, control, and navigation systems that can adapt in real time to guarantee effective and safe operations. This review focuses on obstacle avoidance systems in autonomous farming machinery, highlighting multi-functional capabilities within intricate field settings. It analyzes various sensing technologies, LiDAR, visual cameras, radar, ultrasonic sensors, GPS/GNSS, and inertial measurement units (IMU) for their individual and collective contributions to precise obstacle detection in fluctuating field conditions. The review examines the potential of multi-sensor fusion to enhance detection accuracy and reliability, with a particular emphasizing on achieving seamless obstacle recognition and response. It addresses recent advancements in control and navigation systems, particularly focusing on path-planning algorithms and real-time decision-making. It enables autonomous systems to adjust dynamically across multi-functional agricultural environments. The methodologies used for path planning, including adaptive and learning-based strategies, are discussed for their ability to optimize navigation in complicated field conditions. Real-time decision-making frameworks are similarly evaluated for their capacity to provide prompt, data-driven reactions to changing obstacles, which is critical for maintaining operational efficiency. Moreover, this review discusses environmental and topographical challenges like variable terrain, unpredictable weather, complex crop arrangements, and interference from co-located machinery that hinder obstacle detection and necessitate adaptive, resilient system responses. In addition, the paper emphasizes future research opportunities, highlighting the significance of advancements in multi-sensor fusion, deep learning for perception, adaptive path planning, model-free control strategies, artificial intelligence, and energy-efficient designs. Enhancing obstacle avoidance systems enables autonomous agricultural machinery to transform modern farming by increasing efficiency, precision, and sustainability. The review highlights the potential of these technologies to support global efforts for sustainable agriculture and food security, aligning agricultural innovation with the needs of a swiftly growing population.