Taehoon Kim, Sehun Lee, Junho Ahn^*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-30, 2026, DOI:10.32604/cmc.2025.071597 - 09 December 2025

Abstract As large, room-scale environments become increasingly common, their spatial complexity increases due to variable, unstructured elements. Consequently, demand for room-scale service robots is surging, yet most technologies remain corridor-centric, and autonomous navigation in expansive rooms becomes unstable even around static obstacles. Existing approaches face several structural limitations. These include the labor-intensive requirement for large-scale object annotation and continual retraining, as well as the vulnerability of vanishing point or line-based methods when geometric cues are insufficient. In addition, the high cost of LiDAR and 3D perception errors caused by limited wall cues and dense interior clutter… More >

Zongdong Du^1,2, Xuefeng Feng³, Feng Li³, Qinglong Xian³, Zhenhong Jia^1,2,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2607-2627, 2024, DOI:10.32604/cmc.2024.056616 - 18 November 2024

Abstract The importance of unmanned aerial vehicle (UAV) obstacle avoidance algorithms lies in their ability to ensure flight safety and collision avoidance, thereby protecting people and property. We propose UAD-YOLOv8, a lightweight YOLOv8-based obstacle detection algorithm optimized for UAV obstacle avoidance. The algorithm enhances the detection capability for small and irregular obstacles by removing the P5 feature layer and introducing deformable convolution v2 (DCNv2) to optimize the cross stage partial bottleneck with 2 convolutions and fusion (C2f) module. Additionally, it reduces the model’s parameter count and computational load by constructing the unite ghost and depth-wise separable… More >

Fawad Salam Khan^1,4, Mohd Norzali Haji Mohd^1,*, Saiful Azrin B. M. Zulkifli², Ghulam E Mustafa Abro², Suhail Kazi³, Dur Muhammad Soomro¹

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5741-5759, 2022, DOI:10.32604/cmc.2022.024927 - 21 April 2022

Abstract The evident change in the design of the autopilot system produced massive help for the aviation industry and it required frequent upgrades. Reinforcement learning delivers appropriate outcomes when considering a continuous environment where the controlling Unmanned Aerial Vehicle (UAV) required maximum accuracy. In this paper, we designed a hybrid framework, which is based on Reinforcement Learning and Deep Learning where the traditional electronic flight controller is replaced by using 3D hand gestures. The algorithm is designed to take the input from 3D hand gestures and integrate with the Deep Deterministic Policy Gradient (DDPG) to receive… More >