TY - UNPB
T1 - Algorithmic Optimisation in Cloud Robotics
T2 - A Systematic Review of Implementation Barriers and Performance Gains
AU - Enang, Imo
AU - Omeihe, King
PY - 2025/4
Y1 - 2025/4
N2 - This study conducts a systematic analysis of algorithmic optimisation approaches in cloud robotics systems, examining both performance gains and implementation barriers. Drawing from publicly available datasets from IEEE Dataport, Kaggle, and seminal reviews in the field, we analyze performance across five key metrics: task completion time, resource utilization, energy efficiency, failure rate, and adaptation time. Our findings reveal that hybrid cloud-edge architectures consistently outperform other approaches, achieving up to 67% improvement in task completion time and 73% in energy efficiency compared to conventional methods. However, implementation barriers—particularly system interoperability and network latency issues—significantly constrain the realization of these theoretical gains in practical deployments. We develop an integrated implementation framework that aligns algorithmic selection with contextual requirements and provides staged adoption pathways to maximize real-world benefits while minimizing implementation risks. This research bridges the gap between theoretical optimization models and practical deployment considerations, offering actionable guidance for cloud robotics implementers
AB - This study conducts a systematic analysis of algorithmic optimisation approaches in cloud robotics systems, examining both performance gains and implementation barriers. Drawing from publicly available datasets from IEEE Dataport, Kaggle, and seminal reviews in the field, we analyze performance across five key metrics: task completion time, resource utilization, energy efficiency, failure rate, and adaptation time. Our findings reveal that hybrid cloud-edge architectures consistently outperform other approaches, achieving up to 67% improvement in task completion time and 73% in energy efficiency compared to conventional methods. However, implementation barriers—particularly system interoperability and network latency issues—significantly constrain the realization of these theoretical gains in practical deployments. We develop an integrated implementation framework that aligns algorithmic selection with contextual requirements and provides staged adoption pathways to maximize real-world benefits while minimizing implementation risks. This research bridges the gap between theoretical optimization models and practical deployment considerations, offering actionable guidance for cloud robotics implementers
KW - cloud robotics
KW - algorithmic optimisation
KW - resource allocation
KW - implementation barriers
KW - hybrid cloud-edge computing
M3 - Working paper
BT - Algorithmic Optimisation in Cloud Robotics
ER -