Safe navigation of a quadrotor UAV with uncertain dynamics and guaranteed collision avoidance using barrier Lyapunov function

Habibi, Hamed; Safaei, Ali; Voos, Holger; Darouach, Mohamed; Sánchez-López, José Luis

doi:10.1016/j.ast.2022.108064

Cited by 15 publications

(8 citation statements)

References 43 publications

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“…A novel approach for safe autonomous motion control of a UAV in the presence of disturbances and moving obstacles combines a backstepping-based control design approach with obstacle avoidance [72]. To ensure safety, a Barrier Lyapunov Function (BLF) is directly incorporated into the translational control to keep the vehicle outside of a safety sphere constructed around the obstacles, while directing it towards a desired position.…”

Section: B Control Systemmentioning

confidence: 99%

Exploring the Technical Advances and Limits of Autonomous UAVs for Precise Agriculture in Constrained Environments

Miled,

Grande,

et al. 2023

2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)

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In the field of precise agriculture with autonomous unmanned aerial vehicles (UAVs), the utilization of drones holds significant potential to transform crop monitoring, management, and harvesting techniques. However, despite the numerous benefits of UAVs in smart farming, there are still several technical challenges that need to be addressed in order to render their widespread adoption possible, especially in constrained environments. This paper provides a study of the technical aspect and limitations of autonomous UAVs in precise agriculture applications for constrained environments.

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Section: B Control Systemmentioning

confidence: 99%

Exploring the Technical Advances and Limits of Autonomous UAVs for Precise Agriculture in Constrained Environments

Miled,

Grande,

et al. 2023

2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)

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“…RL has been widely integrated into the field of UAV autonomous navigation, including missions such as target tracking [11], swarm control [12], collision avoidance [13,14], etc. Different RL methods are suitable for different tasks.…”

Section: Introductionmentioning

confidence: 99%

UAV Multi-Dynamic Target Interception: A Hybrid Intelligent Method Using Deep Reinforcement Learning and Fuzzy Logic

Xia,

Mantegh,

Xie

2024

Drones

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With the rapid development of Artificial Intelligence, AI-enabled Uncrewed Aerial Vehicles have garnered extensive attention since they offer an accessible and cost-effective solution for executing tasks in unknown or complex environments. However, developing secure and effective AI-based algorithms that empower agents to learn, adapt, and make precise decisions in dynamic situations continues to be an intriguing area of study. This paper proposes a hybrid intelligent control framework that integrates an enhanced Soft Actor–Critic method with a fuzzy inference system, incorporating pre-defined expert experience to streamline the learning process. Additionally, several practical algorithms and approaches within this control system are developed. With the synergy of these innovations, the proposed method achieves effective real-time path planning in unpredictable environments under a model-free setting. Crucially, it addresses two significant challenges in RL: dynamic-environment problems and multi-target problems. Diverse scenarios incorporating actual UAV dynamics were designed and simulated to validate the performance in tracking multiple mobile intruder aircraft. A comprehensive analysis and comparison of methods relying solely on RL and other influencing factors, as well as a controller feasibility assessment for real-world flight tests, are conducted, highlighting the advantages of the proposed hybrid architecture. Overall, this research advances the development of AI-driven approaches for UAV safe autonomous navigation under demanding airspace conditions and provides a viable learning-based control solution for different types of robots.

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Section: Introductionmentioning

confidence: 99%

“…From the study, it can be found that BLF is an effective tool to deal with state constraints [36]. However, references [35,36] only considered output constraints and ignored the influence of input saturation. In summary, few studies considered input-output constraints for quadrotor UAVs with internal uncertainties and external unknown bounded disturbances.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Backstepping Control of Quadrotor UAVs with Output Constraints and Input Saturation

Wan

et al. 2023

Applied Sciences

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The control performance of quadrotor unmanned aerial vehicles (UAVs) in complex environments can be affected by external disturbances and other factors. In this paper, an adaptive neural network backstepping controller based on the barrier Lyapunov function (BLF) is designed for a quadrotor UAV with internal uncertainties, input–output constraints and external disturbances. Radial basis function neural networks are used to approximate the uncertainties in the dynamic model of the UAV, while the minimum parameter learning method is combined to accelerate the adjustment speed of neural network weights. A robust term is designed to balance the total system disturbance and improve the anti-interference performance. The BLF is used to handle the output constraint so that the constrained parameters cannot break the predefined constraints. An auxiliary system is introduced to solve input saturation and avoid the dependence of tracking error on the input amplitude in the method of approximating input saturation using the smoothing function. The stability of the control system is demonstrated by the Lyapunov method. The simulation results show that the proposed method has high tracking accuracy compared with the backstepping dynamic surface control method, and the input and output are in the predefined range.

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Safe navigation of a quadrotor UAV with uncertain dynamics and guaranteed collision avoidance using barrier Lyapunov function

Cited by 15 publications

References 43 publications

Exploring the Technical Advances and Limits of Autonomous UAVs for Precise Agriculture in Constrained Environments

Exploring the Technical Advances and Limits of Autonomous UAVs for Precise Agriculture in Constrained Environments

UAV Multi-Dynamic Target Interception: A Hybrid Intelligent Method Using Deep Reinforcement Learning and Fuzzy Logic

Adaptive Backstepping Control of Quadrotor UAVs with Output Constraints and Input Saturation

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