In this paper, we introduce a fault-tolerant multi-agent reinforcement learning framework called SERT-DQN to optimize the operations of UAVs with UGV central control in coverage path planning missions. Our approach leverages dual learning systems that combine individual agent autonomy with centralized strategic planning, thus enhancing the efficiency of cooperative path planning missions. This framework is designed for high performance in environments with fault uncertainty detected and operational challenges such as interruptions in connectivity and compromised sensor reliability. With the integration of an innovative communication system between agents, our system appropriately handles both static and dynamic environments. Also, we introduce similarity-based shared experience replay to attain faster convergence and sample efficiency in the multi-agent system. The architecture is specially designed to respond adaptively to such irregularities by effectively showing enhanced resilience in scenarios where data integrity is impaired due to faults or the UAV faces disruptions. Simulation results indicate that our fault tolerance algorithms are very resilient and do indeed improve mission outcomes, especially under dynamic and highly uncertain operating conditions. This approach becomes critical for the most recent sensor-based research in autonomous systems.

Aerial robots (drones) offer critical advantages in missions where human participation is impeded due to hazardous conditions. Among these, search and rescue missions in disaster-stricken areas are particularly challenging due to the dynamic and unpredictable nature of the environment, often compounded by the lack of reliable environmental models and limited ground system communication. In such scenarios, autonomous aerial robots’ operation becomes essential. This paper introduces a novel hierarchical reinforcement learning-based algorithm to address the critical limitation of the aerial robot’s battery life. Central to our approach is the integration of a long short-term memory (LSTM) model, designed for precise battery consumption prediction. This model is incorporated into our HRL framework, empowering a high-level controller to set feasible and energy-efficient goals for a low-level controller. By optimizing battery usage, our algorithm enhances the aerial robot’s ability to deliver rescue packs to multiple survivors without the frequent need for recharging. Furthermore, we augment our HRL approach with hindsight experience replay at the low level to improve its sample efficiency.

The increasing deployment of CubeSats in space missions necessitates the development of efficient and reliable orbital maneuvering techniques, particularly given the constraints on fuel capacity and computational resources. This paper presents a novel two-level control architecture designed to enhance the accuracy and robustness of CubeSat orbital maneuvers. The proposed method integrates a J₂-optimized sequence at the high level to leverage natural perturbative effects for fuel-efficient orbit corrections, with a gated recurrent unit (GRU)-based low-level controller that dynamically adjusts the maneuver sequence in real-time to account for unmodeled dynamics and external disturbances. A Kalman filter is employed to estimate the pointing accuracy, which represents the uncertainties in the thrust direction, enabling the GRU to compensate for these uncertainties and ensure precise maneuver execution. This integrated approach significantly enhances both the positional accuracy and fuel efficiency of CubeSat maneuvers. Unlike traditional methods, which either rely on extensive pre-mission planning or computationally expensive control algorithms, our architecture efficiently balances fuel consumption with real-time adaptability, making it well-suited for the resource constraints of CubeSat platforms. The effectiveness of the proposed approach is evaluated through a series of simulations, including an orbit correction scenario and a Monte Carlo analysis. The results demonstrate that the integrated J₂-GRU system significantly improves positional accuracy and reduces fuel consumption compared to traditional methods. Even under conditions of high uncertainty, the GRU-based control layer effectively compensates for errors in thrust direction, maintaining a low miss distance throughout the maneuvering period. Additionally, the GRU’s simpler architecture provides computational advantages over more complex models such as long short-term memory (LSTM) networks, making it more suitable for onboard CubeSat implementations.

This paper presents a novel control framework that integrates the stability insights of traditional control with reinforcement learning (RL) to tackle uncertain nonlinear dynamic systems. By embedding an explicit Lyapunov-decrease term in the RL reward and shaping the reward with sliding-mode control concepts, the policy learns stability-aware and robustness-promoting behavior, optimizing tracking accuracy and energy efficiency while promoting practical stability under uncertainty. The method is applied to the underactuated satellite attitude control problem using magnetorquers, where inherent under-actuation and external uncertainties present challenges. Simulations, complemented by hardware-in-the-loop experiments, demonstrate that the proposed RL controller achieves better performance and robustness compared to traditional control strategies. These results underscore the potential of combining robust control theory with data-driven learning techniques to enhance the safety and efficacy of control systems in aerospace and robotic applications.