Unmanned Aircraft Vector Field Path Following with Arrival Angle Control

The trajectory control problem, defined as making a vehicle follow a pre-established path in space, can be solved by means of trajectory tracking or path following. In the trajectory tracking problem a timed reference position is tracked. The path following approach removes any time dependence of the problem, resulting in many advantages on the control performance and design. An exhaustive review of path following algorithms applied to quadrotor vehicles has been carried out, the most relevant are studied in this paper. Then, four of these algorithms have been implemented and compared in a quadrotor simulation platform: Backstepping and Feedback Linearisation control-oriented algorithms and NLGL and Carrot-Chasing geometric algorithms.

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“…(40) and the β functions conform the (4x4) decoupling matrix of the system, which is obtained in Eq. (41).…”

Section: Algorithm 1 Backsteppingmentioning

confidence: 99%

“…[35][54][41][83] for path following Vector Field implementations applied on different types of aerial vehicles. In…”

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confidence: 99%

A Survey of Path Following Control Strategies for UAVs Focused on Quadrotors

Rubí

Pérez

Morcego

2019

J Intell Robot Syst

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“…In this approach, the UAV closely orbit around the target maintaining sensor coverage and remaining outside a critical threat range for the persistent tracking. In [14][15][16][17], vector field-based approaches were proposed to guide the UAV to a stable vector field around a target. Zhu et al and Shames et al [18,19] addressed a tracking problem to make the UAV loiter around a target such that both the target estimator and the control systems are stable.…”

Section: Previous Workmentioning

confidence: 99%

“…|| ≤ r f t p and p p p is not overlapped with any buildings (16) where r f tg is the radius of the candidate region and is given as…”

Section: Target Modelmentioning

confidence: 99%

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Information-driven Persistent Sensing of a Non-cooperative Mobile Target Using UAVs

Shin

García

Álvarez

2017

J Intell Robot Syst

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This paper addresses the persistent sensing problem of moving ground targets of interest using a group of fixed wing UAVs. Especially, we aim to overcome the challenge of physical obscuration in complex mission environments. To this end, the persistent sensing problem is formulated under an optimal control framework, i.e. deploying and managing UAVs in a way maximising the visibility to the non-cooperative target.The main issue with such a persistent sensing problem is that it generally requires the knowledge of future target positions, which is uncertain. To mitigate this issue, a probabilistic map of the future target position is widely utilised. However, most of the probabilistic models use only limited information of the target. This paper proposes an innovative framework that can make the best use of all available information, not only limited information. For the validation of the feasibility, the performance of the proposed framework is tested in a Manhattan-type controlled urban environment. All the simulation tests use the same framework proposed, but utilise different level of information. The simulation results confirm that the performance of the persistent sensing significantly improves, up to 30%, when incorporating all available target information.

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