Coordinating heterogeneous aerial and ground vehicles with fundamentally different dynamic time-scales is a longstanding challenge in multi-agent robotics. This paper proposes a distributed reinforcement learning (DRL) framework for cooperative Unmanned Aerial Vehicle (UAV) and Unmanned Ground Vehicle (UGV) formation control that explicitly accounts for multirate dynamics: UAVs execute high-frequency control at 10 Hz while UGVs operate at 2 Hz. Each agent maintains a local actor-critic network trained via a variant of MultiAgent Proximal Policy Optimisation (MAPPO) augmented with a consensus-based communication graph and a multirate synchronisation module that bridges the temporal mismatch between the two vehicle classes. A shaped reward formulation penalises formation deviation, inter-agent collisions and communication dropout simultaneously. Simulation experiments In a physicsaccurate Gazebo/ROS2 environment with two UAV leaders, two UAV followers, and two UGVs demonstrate a task success rate of 96.4%, a formation accuracy of 97.1%, and a collision rate of only 0.8%, outperforming MADDPG, MAPPO (singlerate), centralised DDPG and PID baselines by margins of up to 24.9 percentage points. The framework is further validated in a disaster-response scenario with a dynamic obstacle fields confirming its suitability for real-world heterogeneous multirobot missions.
Sankara Rao, P. Palpandi, G. Beulah et al.· International Conference on...· 0 citations
Accurate network traffic forecasting is fundamental to Quality of Service enforcement, proactive congestion control, and dynamic resource allocation in modern backbone and software-defined networks. However, existing approaches often lack adaptability to non-stationary traffic patterns and fail to provide a consistent comparative evaluation across diverse models under unified experimental conditions. This paper presents an adaptive, data-driven framework that integrates Bidirectional LSTM (Bi-LSTM), LSTM, Gated Recurrent Units (GRU), Random Forest, XGBoost, Support Vector Regression (SVR), and classical ARIMA regressors for short- and medium-term traffic forecasting. The proposed architecture couples multi-scale temporal feature extraction with a feedback-driven online retraining loop, enabling continuous adaptation to distributional shifts. Extensive experiments are conducted on two publicly available datasets CAIDA Equinix backbone traces and the MAWI traffic archive comprising over 72 hours of flow-level measurements at one-minute resolution. Bi-LSTM achieves the lowest RMSE of 0.0287 Gbps and the highest $R^{2}=0.9714$, outperforming ARIMA by $\mathbf{7 5 . 2 \%}$ and vanilla LSTM by $\mathbf{8 . 0 \%}$. All results are confirmed via paired Diebold-Mariano (DM) tests and Student’s t-tests $(p \lt 0.01)$. System inference latency of 2.3 ms per batch satisfies real-time SDN control-plane requirements. Code and preprocessing scripts will be made publicly available to ensure full reproducibility.
E. Chithra, G. C. Bharathi, Sankara Rao et al.· International Conference on...· 0 citations