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
The fast development of internet of things (IoT) networks has led to an increased probability of cyberattacks. Intrusion detection systems (IDS) are needed for identifying unauthorised access and malicious activities in such dynamic environments. However, existing machine learning (ML) models failed to handle the complexity and variability of modern cyber threats. In this work, a hybrid deep learning (DL)-based anomaly detection model is presented for IoT cybersecurity. The model combines three types of features: (i) supervised feature extraction using linear discriminant analysis (LDA) to extract the most discriminative features, (ii) unsupervised feature learning through autoencoders to capture latent representations of the input data, and (iii) statistical features such as mean, variance, skewness, and kurtosis to learn input characteristics. The fused feature matrix is fed into a learning based echo state network (LBESN) for final detection. The parameters of the LBESN model are tuned using black eagle optimizer (BEO). Experimental results on standard intrusion detection datasets such as UNSW-NB15, KDD99, and InSDN show that the proposed model achieves superior performance in terms of accuracy, precision, recall, and F1-score compared to conventional DL techniques.
P. Palpandi, B. Sakthivel, M. Ponnrajakumari et al.· International Journal of Inf...· 0 citations