General Purpose Low-Level Reinforcement Learning Control for Multi-Axis Rotor Aerial Vehicles

White shark optimizer with optimal deep learning based effective unmanned aerial vehicles communication and scene classification

Unmanned aerial vehicles (UAVs) become a promising enabler for the next generation of wireless networks with the tremendous growth in electronics and communications. The application of UAV communications comprises messages relying on coverage extension for transmission networks after disasters, Internet of Things (IoT) devices, and dispatching distress messages from the device positioned within the coverage hole to the emergency centre. But there are some problems in enhancing UAV clustering and scene classification using deep learning approaches for enhancing performance. This article presents a new White Shark Optimizer with Optimal Deep Learning based Effective Unmanned Aerial Vehicles Communication and Scene Classification (WSOODL-UAVCSC) technique. UAV clustering and scene categorization present many deep learning challenges in disaster management: scene understanding complexity, data variability and abundance, visual data feature extraction, nonlinear and high-dimensional data, adaptability and generalization, real-time decision making, UAV clustering optimization, sparse and incomplete data. the need to handle complex, high-dimensional data, adapt to changing environments, and make quick, correct decisions in critical situations drives deep learning in UAV clustering and scene categorization. The purpose of the WSOODL-UAVCSC technique is to cluster the UAVs for effective communication and scene classification. The WSO algorithm is utilized for the optimization of the UAV clustering process and enables to accomplish effective communication and interaction in the network. With dynamic adjustment of the clustering, the WSO algorithm improves the performance and robustness of the UAV system. For the scene classification process, the WSOODL-UAVCSC technique involves capsule network (CapsNet) feature extraction, marine predators algorithm (MPA) based hyperparameter tuning, and echo state network (ESN) classification. A wide-ranging simulation analysis was conducted to validate the enriched performance of the WSOODL-UAVCSC approach. Extensive result analysis pointed out the enhanced performance of the WSOODL-UAVCSC method over other existing techniques. The WSOODL-UAVCSC method achieved an accuracy of 99.12%, precision of 97.45%, recall of 98.90%, and F1-score of 98.10% when compared to other existing techniques.

A Reinforcement Learning Based Dirt-Exploration for Cleaning-Auditing Robot

Cleaning is one of the fundamental tasks with prime importance given in our day-to-day life. Moreover, the importance of cleaning drives the research efforts towards bringing leading edge technologies, including robotics, into the cleaning domain. However, an effective method to assess the quality of cleaning is an equally important research problem to be addressed. The primary footstep towards addressing the fundamental question of “How clean is clean” is addressed using an autonomous cleaning-auditing robot that audits the cleanliness of a given area. This research work focuses on a novel reinforcement learning-based experience-driven dirt exploration strategy for a cleaning-auditing robot. The proposed approach uses proximal policy approximation (PPO) based on-policy learning method to generate waypoints and sampling decisions to explore the probable dirt accumulation regions in a given area. The policy network is trained in multiple environments with simulated dirt patterns. Experiment trials have been conducted to validate the trained policy in both simulated and real-world environments using an in-house developed cleaning audit robot called BELUGA.

Unmanned Aerial Vehicle Pitch Control under Delay Using Deep Reinforcement Learning with Continuous Action in Wind Tunnel Test

Nonlinear flight controllers for fixed-wing unmanned aerial vehicles (UAVs) can potentially be developed using deep reinforcement learning. However, there is often a reality gap between the simulation models used to train these controllers and the real world. This study experimentally investigated the application of deep reinforcement learning to the pitch control of a UAV in wind tunnel tests, with a particular focus of investigating the effect of time delays on flight controller performance. Multiple neural networks were trained in simulation with different assumed time delays and then wind tunnel tested. The neural networks trained with shorter delays tended to be susceptible to delay in the real tests and produce fluctuating behaviour. The neural networks trained with longer delays behaved more conservatively and did not produce oscillations but suffered steady state errors under some conditions due to unmodeled frictional effects. These results highlight the importance of performing physical experiments to validate controller performance and how the training approach used with reinforcement learning needs to be robust to reality gaps between simulation and the real world.