Deep reinforcement learning (DRL), a vital branch of artificial intelligence, has shown great promise in mobile robot navigation within dynamic environments. However, existing studies mainly focus on simplified dynamic scenarios or the modeling of static environments, which results in trained models lacking sufficient generalization and adaptability when faced with real-world dynamic environments, particularly in handling complex task variations, dynamic obstacle interference, and multimodal data fusion. Addressing these gaps is essential for enhancing its real-time performance and versatility. Through a comparative analysis of classical DRL algorithms, this study highlights their advantages and limitations in handling real-time navigation tasks under dynamic environmental conditions. In particular, the paper systematically examines value-based, policy-based, and hybrid-based DRL methods, discussing their applicability to different navigation challenges. Additionally, by reviewing recent studies from 2021 to 2024, it identifies key trends in DRL-based navigation, revealing a strong focus on indoor environments while outdoor navigation and multi-robot collaboration remain underexplored. The analysis also highlights challenges in real-world deployment, particularly in sim-to-real transfer and sensor fusion. Based on these findings, this paper outlines future directions to enhance real-time adaptability, multimodal perception, and collaborative learning frameworks, providing theoretical and technical insights for advancing DRL in dynamic environments.

This study researches the usage of YOLOv4 for real-time loose fruit detection in oil palm plantations as the first step in implementing automation in the collection of loose fruits. Our system leverages high-resolution video data (4K and 1080p) from various plantation settings. To address the challenges of small and numerous loose fruits, we introduced an image preprocessing technique called “image tiling” into the vision system workflow and studied the effects this has on the performance of the detection model. This involves dividing the image into smaller sections for individual processing by both YOLOv4 and YOLOv4-tiny models, enhancing detection accuracy. Refined models (YOLOv4-tiling and YOLOv4-tiny-tiling) are then evaluated. YOLOv4 achieved the highest precision (97%) and F1-score (86.3%), while YOLOv4-tiling offered a slight improvement in recall (80.8%). Notably, YOLOv4-tiny, initially underperforming (precision: 37.2%, recall: 20.9%, F1-score: 25%), showed significant improvement with tiling (precision: 90.5%, recall: 67.1%, F1-score: 73.8%). Also, replacing the SPP layer in YOLOv4 with SPP-Fast resulted in increased precision (92.6%) and a significantly improved F1-score of 91.4%. This vision system was then integrated with a custom designed Loose Fruit Collector Robot through the Robot Operating System (ROS).

Numerous control strategies are used to optimize the performance of the three-phase rectifier. Sliding Mode Control (SMC) is chosen for its robustness, fast response, high-efficiency control, finite-time convergence, and other features. Several SMC methods utilized with three-phase rectifiers are outlined in this review study. This study is divided into two main parts; the first part discusses the technique used to optimize the performance of the rectifier, while the second part discusses the SMC algorithms used to enhance the performance of the rectifier. In addition to their theoretical components, this review paper investigates the practical implementation challenges of SMCs for three-phase rectifiers. This paper discusses the optimization parameters of SMC and stability and load fluctuation in the rectifier system. It introduces state-of-the-art algorithms that enhance the rectifier’s dynamic performance, including the adaptive and observation algorithms. This paper is vital for engineers, researchers, and experts focusing on the SMC of three-phase rectifiers. To our knowledge, no similar review of three-phase rectifiers is available for SMC. Finally, a research gap is identified – as far as we know, no predefined time (free-will arbitrary time) SMC algorithm is used to control the performance of any power electronic device, such as a three-phase rectifier. This active rectifier is used in our project to optimize the power system for the alternator used in our unmanned ground vehicle (UGV) as an oil palm autonomous grabber.