Autonomous trajectory planning for space vehicles with a Newton–Kantorovich/convex programming approach

Xiao-ming, Cheng; Li, Huifeng; Zhang, Ran

doi:10.1007/s11071-017-3626-7

Cited by 15 publications

(4 citation statements)

References 29 publications

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“…In recent years, a convex optimization technique has gained increasing popularity in solving multiple optimization problems of space missions, such as energy management (Murgovski et al, 2013) and optimal control (Cheng et al, 2017). As for application in aerospace missions including powered landing vehicles (Wang et al, 2019a(Wang et al, , 2019b, spacecraft (Wang and Grant, 2018), hypersonic vehicles (Pan et al, 2016), the convex optimization technique is widely studied.…”

Section: Introductionmentioning

confidence: 99%

Online trajectory optimization and guidance algorithm for space interceptors with nonlinear terminal constraints via convex programming

Sun

Chen

2022

AEAT

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Purpose In this paper, an online convex optimization method for the exoatmospheric ascent trajectory of space interceptors is proposed. The purpose of this paper is to transform the original trajectory optimization problem into a sequence of convex optimization subproblems. Design/methodology/approach For convenience in calculating accuracy and efficiency, the complex nonlinear terminal orbital elements constraints are converted into several quadratic equality constraints, which can be better computed by a two-step correction method during the iteration. First, the nonconvex thrust magnitude constraint is convexified by the lossless convexification technique. Then, discretization and successive linearization are introduced to transform the original problem into a sequence of one convex optimization subproblem, considering different flight phases. Parameters of trust-region and penalty are also applied to improve the computation performance. To correct the deviation in real time, the iterative guidance method is applied before orbit injection. Findings Numerical experiments show that the algorithm proposed in this paper has good convergence and accuracy. The successive progress can converge in a few steps and 3–4 s of CPU time. Even under engine failure or mission change, the algorithm can yield satisfactory results. Practical implications The convex optimization method presented in this paper is expected to generate a reliable optimal trajectory rapidly in different situations and has great potential for onboard applications of space interceptors. Originality/value The originality of this paper lies in the proposed online trajectory optimization method and guidance algorithm of the space inceptors, especially for onboard applications in emergency situations.

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Section: Introductionmentioning

confidence: 99%

Online trajectory optimization and guidance algorithm for space interceptors with nonlinear terminal constraints via convex programming

Sun

Chen

2022

AEAT

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“…Zhou et al ( 21 ) propose a trajectory planning method in a ramp scenario and convert the nonlinear planning problem to a linear problem, and then use a linear solver to solve the optimal trajectory. However, directly converting the planning problem into a nonlinear programming problem is likely to lead to local minimum or trajectory instability ( 22 , 23 ). Therefore, many hierarchical planning frameworks are mentioned, which usually start with a rough solution of the trajectory in the first stage, which is used to initialize the nonlinear programming problem in the second stage, thus ensuring a more stable trajectory ( 24 , 25 ).…”

mentioning

confidence: 99%

Trajectory Planning Framework Combining Replanning and Hierarchical Planning

Qin,

Liu,

Shang

et al. 2023

Transportation Research Record: Journal of the Transportation R

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To enable autonomous vehicles to respond quickly to changes in the surrounding environment and to generate safe and stable trajectory, we propose a safety-based hierarchical trajectory planning method that divides the planning module into two parts: the planner and the replanning monitor. In the planner, we first propose a fast linear trajectory planning method that uses the mass point model instead of the vehicle model and linearizes the collision avoidance constraint using the Big-M method for linear programming (Big-M) to obtain a linear programming model. The complete vehicle kinematic model is then built in the nonlinear programming stage, the collision constraints and cost functions are refined, and the rough solution of the linear programming is brought into it to obtain the exact solution. The replanning monitor is divided into safety and comfort monitors. The safety monitor will always pay attention to the changes in the surrounding environment and calculate whether the vehicle is in danger of collision in the future period, while the comfort monitor is more concerned with the comfort of driving the vehicle; when the monitor requirements are not met, replanning will be carried out. By simulating the driving environment, the proposed algorithm can form a safe and comfortable trajectory, which verifies the rationality of the proposed method.

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“…Off-line algorithms has also been intensively studied. The mathematical model based algorithms [13,3,1] and the bio-inspired algorithms [4,10,6] are such type of algorithms. The computational complexity for the off-line algorithms is normally higher than that of the online algorithms.…”

mentioning

confidence: 99%

“…However, a better performance can be achieved. Mathematical model based algorithms formulate the path planning problem into mathematical programs, such as mixed-integer linear program [13], binary linear program [3] and convex program [1]. The same problem can also be solved by using the bio-inspired algorithms.…”

mentioning

confidence: 99%

A control parametrization based path planning method for the quad-rotor uavs

Guo¹,

Li²,

Ji³

2022

JIMO

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A time optimal path planning problem for the Quad-rotor unmanned aerial vehicles (UAVs) is investigated in this paper. A 3D environment with obstacles is considered, which makes the problem more challenging. To tackle this challenge, the problem is formulated as a nonlinear optimal control problem with continuous state inequality constraints and terminal equality constraints. A control parametrization based method is proposed. Particularly, the constraint transcription method together with a local smoothing technique is utilized to handle the continuous inequality constraints. The original problem is then transformed into a nonlinear program. The corresponding gradient formulas for both of the cost function and the constraints are derived, respectively. Simulation results show that the proposed path planning method has less tracking error than that of the rapid-exploring random tree (RRT) algorithm and that of the A star algorithm. In addition, the motor speed has less changes for the proposed algorithm than that of the other two algorithms.

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Autonomous trajectory planning for space vehicles with a Newton–Kantorovich/convex programming approach

Cited by 15 publications

References 29 publications

Online trajectory optimization and guidance algorithm for space interceptors with nonlinear terminal constraints via convex programming

Online trajectory optimization and guidance algorithm for space interceptors with nonlinear terminal constraints via convex programming

Trajectory Planning Framework Combining Replanning and Hierarchical Planning

A control parametrization based path planning method for the quad-rotor uavs

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