Optimization-Based Safety Analysis of Obstacle Avoidance Systems for Unmanned Aerial Vehicles

Srikanthakumar, Sivaranjini; Liu, Cunjia; Chen, Wen‐Hua

doi:10.1007/s10846-011-9586-0

Cited by 18 publications

(4 citation statements)

References 19 publications

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“…This work is an extension of the work presented in ref. [10], where the optimization-based verification process has been proposed for a small scale unmanned aircraft. In addition to looking into a different application, there are two main extensions in this paper: Firstly, the moving object avoidance is considered in this paper which makes the prediction of the possible worst cases more complicated and challenging; secondly, the uncertainties in sensor measurements are considered in this paper.…”

Section: Contributions Of This Papermentioning

confidence: 99%

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Worst-case analysis of moving obstacle avoidance systems for unmanned vehicles

Srikanthakumar

Chen

2014

Robotica

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SUMMARYThis paper investigates worst-case analysis of a moving obstacle avoidance algorithm for unmanned vehicles in a dynamic environment in the presence of uncertainties and variations. Automatic worst-case search algorithms are developed based on optimization techniques, and illustrated by a Pioneer robot with a moving obstacle avoidance algorithm developed using the potential field method. The uncertainties in physical parameters, sensor measurements, and even the model structure of the robot are taken into account in the worst-case analysis. The minimum distance to a moving obstacle is considered as an objective function in automatic search process. It is demonstrated that a local nonlinear optimization method may not be adequate, and global optimization techniques are necessary to provide reliable worst-case analysis. The Monte Carlo simulation is carried out to demonstrate that the proposed automatic search methods provide a significant advantage over random sampling approaches.

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Section: Contributions Of This Papermentioning

confidence: 99%

“…Otherwise, the obstacle avoidance algorithm and controller have to be redefined to satisfy the anti-collision condition. 10 Several optimization algorithms are investigated for the verification of the obstacle avoidance algorithms in this paper.…”

Section: Optimization-based Worst-case Analysis Approachmentioning

confidence: 99%

Worst-case analysis of moving obstacle avoidance systems for unmanned vehicles

Srikanthakumar

Chen

2014

Robotica

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“…In the control field, with the rapidly expanding helicopter technology, the unmanned-aerial-vehicle (UAV) helicopter has been of wide concern in recent years; it has been applied to maritime supervision, environmental monitoring, search and rescue, agricultural and forestry protection, pipeline inspection, and aerial photography, to name just a few areas [1]. Since UAV helicopter flight control is a highly nonlinear, strongly coupled, and inherently unstable problem and subject to the uncertainties of various environments and varying flight conditions, the total dynamics of a UAV helicopter system are extremely complex, which have been decomposed into the longitudinal and lateral dynamics in [2][3][4], we only consider the turning movement of the UAV helicopter yaw control system under the comprehensive actions of actuator faults, input saturation, full-state constraints, and external disturbances in this paper. A linearized model cannot fulfill a global model approximation.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Fault-Tolerant Control for the Yaw Control of UAV Helicopters with Input Saturation and Full-State Constraints

Zhang

Chen

2020

Applied Sciences

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In this paper, an adaptive neural fault-tolerant tracking control scheme is presented for the yaw control of an unmanned-aerial-vehicle helicopter. The scheme incorporates a non-affine nonlinear system that manages actuator faults, input saturation, full-state constraints, and external disturbances. Firstly, by using a Taylor series expansion technique, the non-affine nonlinear system is transformed into an affine-form expression to facilitate the desired control design. In comparison with previous techniques, the actuator efficiency is explicit. Then, a neural network is considered to approximate unknown nonlinear functions, and a time-varying barrier Lyapunov function is employed to prevent transgression of the full-state variables using a backstepping technique. Robust adaptive control laws are designed to handle parameter uncertainties and unknown bounded disturbances to cut down the number of learning parameters and simplify the computational burden. Moreover, an auxiliary system is constructed to guarantee the pitch angle of the UAV helicopter yaw control system to satisfy the input constraint. Uniform boundedness of all signals in a closed-loop system is ensured via Lyapunov theory; the tracking error converges to a small neighborhood near zero. Finally, when the numerical simulations are applied to a yaw control of helicopter, the adaptive neural controller demonstrates the effectiveness of the proposed technique.

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“…With the wide application of UAVs in various fields, there is more and more research on UAV flight issues. Most of the academic research on UAVs is devoted to its technical aspects ( 25 – 27 ). In relation to UAV flight risks, Ghasri and Maghrebi have discussed real accidents caused by UAVs and explored the potential safety hazards during UAV flights ( 1 ).…”

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confidence: 99%

Factors Affecting Unmanned Aerial Vehicles’ Unsafe Behaviors and Influence Mechanism Based on Social Network Theory

Wang

Guo

Liu

et al. 2022

Transportation Research Record: Journal of the Transportation R

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With the widespread application of unmanned aerial vehicles (UAVs), flight safety issues have gradually become prominent. To improve the safety level of UAV flight, a conceptual model was constructed through groups of unsafe behaviors of UAV flight based on the Swiss cheese model (reason model). The relationship network model of unsafe behaviors of UAV flight was built after using the two-mode and one-mode social network analysis, and the unsafe behaviors of UAV flight influence mechanism were studied by basic characteristics of network analysis, centrality analysis, core-periphery structure analysis, in/out-degree analysis, and structural hole analysis. The results showed that the two-mode network is closely related: unreasonable safety management structure of the organization and weak supervision of UAV flight operation were those unsafe behaviors of UAV supervision that had great influence. The unsafe behaviors of UAV supervision, such as the organization’s illegal deployment of unqualified personnel for tasks and lack of ground commander for the mission plan, were in the core position of the network. The proposed model can effectively reduce the unsafe behaviors of UAV operations by eliminating critical unsafe behaviors of UAV supervision in the network and reducing UAV flight accidents.

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Optimization-Based Safety Analysis of Obstacle Avoidance Systems for Unmanned Aerial Vehicles

Cited by 18 publications

References 19 publications

Worst-case analysis of moving obstacle avoidance systems for unmanned vehicles

Worst-case analysis of moving obstacle avoidance systems for unmanned vehicles

Adaptive Neural Fault-Tolerant Control for the Yaw Control of UAV Helicopters with Input Saturation and Full-State Constraints

Factors Affecting Unmanned Aerial Vehicles’ Unsafe Behaviors and Influence Mechanism Based on Social Network Theory

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