This article presents a geometric adaptive controller for a quadrotor unmanned aerial vehicle with artificial neural networks. It is assumed that the dynamics of a quadrotor is disturbed by the arbitrary, unstructured forces and moments caused by wind. To address this, the proposed control system is augmented with the multilayer neural networks, and the weights of the neural networks are adjusted online according to an adaptive law. By using the universal approximation theorem, it is shown that the effects of the unknown disturbances can be mitigated. More specifically, under the proposed control system, the tracking errors in the position and heading directions are uniformly ultimately bounded. These are developed directly on the special Euclidean group to avoid the complexities or singularities inherent to local parameterizations. The efficacy of the proposed control system is first illustrated by numerical examples. Then, several indoor flight experiments are presented to demonstrate that the proposed controller successfully rejects the effects of wind disturbances even for aggressive, agile maneuvers.

This paper proposes an adaptive incremental nonlinear dynamic inversion (INDI) controller for unmanned aerial manipulators (UAMs). A novel adaptive law is employed to enable aerial manipulators to manage the inertia parameter changes that occur when the manipulator moves or picks up unknown objects during any phase of the UAM’s flight maneuver. The adaptive law utilizes a Kalman filter to estimate a set of weighting factors employed to adjust the control gain matrix of a previously developed INDI control law formulated for the corresponding UAV (no manipulator included). The proposed adaptive control scheme uses acceleration and actuator input measurements of the UAV without necessitating any knowledge about the manipulator, its movements, or the objects being grasped, thus enabling the use of previously developed INDI UAV controllers for UAMs. The algorithm is validated through simulations demonstrating that the adaptive control gain matrix used in the UAV’s INDI controller is promptly updated based on the UAM maneuvers, resulting in effective UAV and robot arm control.

This paper introduces an adaptive Incremental Nonlinear Dynamic Inversion (INDI) control methodology with guaranteed stability for a highly maneuverable unmanned aerial manipulator (UAM) designed to operate under demanding conditions, such as rapid arm movements and varying manipulated payloads. This work extends previous work on the control of aerial manipulators by addressing control effectiveness uncertainties. The stability bounds of the inertia matrix within the control effectiveness matrix are derived through a detailed eigenvalue analysis, ensuring that the eigenvalues consistently remain within a specified stability threshold. The proposed methodology ensures both stability and control responsiveness by dynamically adjusting the inertia parameters of the control effectiveness matrix within stability-guaranteeing limits. The methodology is validated through extensive simulation tests showing that the proposed adaptive INDI controller outperforms previous UAM controllers, effectively coping with disturbances caused by varying grasped payloads/masses and extended arm movements with guaranteed stability.

This study provides a thorough investigation into autonomous exploration within unknown environments, with a focus on minimizing exploration time and so fuel consumption. This research utilizes a 2D simulation environment to collect training data efficiently, facilitating the evaluation of the proposed methods’ efficiency, adaptability, and generalizability through various experiments. Single-robot autonomous exploration policies using advanced Deep Reinforcement Learning (DRL) algorithms are developed. The main novelty of this paper is the development of risk-sensitive policies, in contrast to traditional risk-neutral approaches in DRL, to enhance exploration efficiency. Additionally, this research presents the development of an adaptive autonomous exploration policy that dynamically adjusts the Conditional Value-at-Risk (CVaR) based on the exploration percentage. The results demonstrate a significant improvement in autonomous exploration efficiency compared to well-known traditional RL and classical single-robot exploration policies, validating the effectiveness of the suggested novel autonomous exploration strategies.