Receding Horizons with Heading Constraints for Collision Avoidance

Gagnon, Éric; Rabbath, C.A.; Lauzon, M.

doi:10.2514/6.2005-6369

Cited by 7 publications

(6 citation statements)

References 9 publications

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“…To achieve collision avoidance with other UAVs, a priority indexing scheme is used [28,29]: all UAVs are tagged and the UAV with a lower index creates an imaginary obstacle around the UAV with a higher index (as seen in Fig. 6) and tries to avoid it.…”

Section: Collision Avoidancementioning

confidence: 99%

Non-linear dual-mode receding horizon control for multiple unmanned air vehicles formation flight based on chaotic particle swarm optimisation

Duan

Liu

2010

IET Control Theory &Amp; Applications

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This study presents a non-linear dual-mode receding horizon control (RHC) approach to investigate the formation flight problem for multiple unmanned air vehicles (UAVs) under complicated environments. A chaotic particle swarm optimisation (PSO)-based non-linear dual-mode RHC method is proposed for solving the constrained non-linear systems. The presented chaotic PSO derives both formation model and its parameter values, and the control sequence is predicted in this way, which can also guarantee the global convergence speed. A dual-model control strategy is used to improve the stability and feasibility for multiple UAVs formation flight controller, and the state-feedback control is also adopted, where the model is based on the invariant set theory. Series experimental results show the feasibility and validity of the proposed control algorithm over other algorithms. The proposed approach is also a promising control strategy in solving other complicated real-world problems.

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Section: Collision Avoidancementioning

confidence: 99%

Non-linear dual-mode receding horizon control for multiple unmanned air vehicles formation flight based on chaotic particle swarm optimisation

Duan

Liu

2010

IET Control Theory &Amp; Applications

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“…This method could be applied to the situations where there were various kinds of constraints and provide a suboptimal solution ultimately. Typical measures were proportional navigation, 3 model predictive control, 4,5 and so forth. Third, the collision avoidance was implemented by probability theory.…”

Section: Introductionmentioning

confidence: 99%

“…Singh 4 programmed an autonomous missile avoidance trajectory for aircrafts in accordance with model predictive control theory, but the saturation of vehicle's turning acceleration was ignored and this method demanded considerable computational efforts. Gagnon et al 5 discussed the avoidance trajectories under several cases in unknown environments according to model predictive control theory. However, the assumptions of these collision cases were simplex.…”

Section: Introductionmentioning

confidence: 99%

“…1) The obstacles' movements in this paper were assumed more closing to the ones in practical environments, while in common literatures, [1][2][3][5][6][7] the obstacles were usually assumed to be static or to conduct uniform linear motion. 2) Multiple constraints were taken into consideration simultaneously.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to inequality constraints caused by obstacle avoidance, terminal constraints, state constraints, and control constraints were coped with together, which extended the preceding research on obstacle avoidance. [1][2][3][5][6][7] 3) A novel method which combined slack variable technique with sliding mode control theory was proposed. Slack variables 10,11 were introduced to transform the inequality constraints into equality constraints.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Suboptimal trajectory programming for unmanned aerial vehicles with dynamic obstacle avoidance

Guo

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part G:

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With regard to the dynamic obstacles current unmanned aerial vehicles encountered in practical applications, an integral suboptimal trajectory programming method was proposed. It tackled with multiple constraints simultaneously while guiding the unmanned aerial vehicle to execute autonomous avoidance maneuver. The kinetics of both unmanned aerial vehicle and dynamic obstacles were established with appropriate hypotheses. Then it was assumed that the unmanned aerial vehicle was faced with terminal constraints and control constraints in the whole duration. Meanwhile, the performance index was established as minimum control efforts. The initial trajectory was generated according to optimized model predictive static programming. Next, the slack variables were introduced to transform the inequality constraints arising from dynamic obstacle avoidance into equality constraints. In addition, sliding mode control theory was utilized to determine these slack variables' dynamics by designing the approaching law of sliding mode. Then the avoidance trajectory for single or multiple dynamic obstacles was developed by this combined method. At last, a further trajectory optimization was conducted by differential dynamic programming. Consequently, the integral problem was solved step by step and numerical simulations demonstrated that the integral method possessed high computational efficiency.

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Heading and position receding horizon control for trajectory generation

Gagnon

Rabbath

Lauzon

Proceedings of the 2005, American Control Conference, 2005.

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Receding Horizons with Heading Constraints for Collision Avoidance

Cited by 7 publications

References 9 publications

Non-linear dual-mode receding horizon control for multiple unmanned air vehicles formation flight based on chaotic particle swarm optimisation

Non-linear dual-mode receding horizon control for multiple unmanned air vehicles formation flight based on chaotic particle swarm optimisation

Suboptimal trajectory programming for unmanned aerial vehicles with dynamic obstacle avoidance

Heading and position receding horizon control for trajectory generation

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