Trajectory Optimization of Space Maneuver Vehicle Using a Hybrid Optimal Control Solver

et al. 2021

IEEE Trans. Cybern.

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Constrained autonomous vehicle overtaking trajectories are usually difficult to be generated due to certain practical requirements and complex environmental limitations. This problem becomes more challenging when multiple contradicting objectives are required to be optimized and the on-road objects to be overtaken are irregularlyplaced. In this paper, a novel swarm intelligence-based algorithm is proposed for producing the multi-objective optimal overtaking trajectory of autonomous ground vehicles. The proposed method solves a multiobjective optimal control model in order to optimize the maneuver time duration, the trajectory smoothness, and the vehicle visibility, while taking into account different types of mission-dependent constraints. However, one problem that could have an impact on the optimization process is the selection of algorithm control parameters. To desensitize the negative influence, a novel fuzzy adaptive strategy is proposed and embedded in the algorithm framework. This allows the optimization process can dynamically balance the local exploitation and global exploration, thereby exploring the trade-off between objectives more effective. The performance of using the designed fuzzy adaptive multi-objective method is analyzed and validated by executing a number of simulation studies. The results confirm the effectiveness of applying the proposed algorithm to produce multi-objective optimal overtaking trajectories for the autonomous ground vehicles. Moreover, the comparison to other stateof-the-art multi-objective optimization schemes shows that the designed strategy tends to be more capable in terms of producing a set of widespread and high-quality pareto-optimal solutions.

Section: G Online Performance Of the Proposed Motion Plannersupporting

confidence: 60%

Section: Introductionmentioning

confidence: 99%

Multiobjective Overtaking Maneuver Planning for Autonomous Ground Vehicles

et al. 2021

IEEE Trans. Cybern.

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“…The state can be obtained by integrating the dynamics numerically. That is, the optimization model becomes [10]: (10) in which (•, •) stands for the general form of inequality constraints, whereas Φ(…”

Section: A Discretized Optimal Parking Modelmentioning

confidence: 99%

“…In this paper, a two-stage optimization structure is used to search the optimal solution of Eq. (10). The motivation for the use of this design philosophy relies on its enhanced convergence and computational ability.…”

Section: B Initial Parking Maneuver Plannermentioning

confidence: 99%

Two-Stage Trajectory Optimization for Autonomous Ground Vehicles Parking Maneuver

et al. 2019

IEEE Trans. Ind. Inf.

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This paper proposes a two-stage optimization framework for generating the optimal parking motion trajectory of autonomous ground vehicles. The motivation for the use of this multi-layer optimization strategy relies on its enhanced convergence ability and computational efficiency in terms of finding optimal solutions under the constrained environment. In the first optimization stage, the designed optimizer applies an improved particle swarm optimization technique to produce a near-optimal parking movement. Subsequently, the motion trajectory obtained from the first stage is used to start the second optimization stage, where gradient-based techniques are applied. The established methodology is tested to explore the optimal parking maneuver for a car-like autonomous vehicle with the consideration of irregularly parked obstacles. Simulation results were produced and comparative studies were conducted for different mission cases. The obtained results not only confirm the effectiveness but also reveal the enhanced performance of the proposed optimization framework.

“…1) The spacecraft trajectory optimization problem established in [27] is reformulated and extended to a new multi-objective reentry optimal control model. Various path constraints including the control rate and obstacle avoidance are considered in the optimization process.…”

Section: Introductionmentioning

confidence: 99%

Solving Multiobjective Constrained Trajectory Optimization Problem by an Extended Evolutionary Algorithm

Xia

et al. 2020

IEEE Trans. Cybern.

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Highly constrained trajectory optimization problems are usually difficult to solve. Due to some real-world requirements, a typical trajectory optimization model may need to be formulated containing several objectives. Because of the discontinuity or nonlinearity in the vehicle dynamics and mission objectives, it is challenging to generate a compromised trajectory that can satisfy constraints and optimize objectives. To address the multi-objective trajectory planning problem, this study applies a specific multiple-shooting discretization technique with the newest NSGA-III optimization algorithm and constructs a new evolutionary optimal control solver. In addition, three constraint handling algorithms are incorporated in this evolutionary optimal control framework. The performance of using different constraint handling strategies is detailed and analyzed. The proposed approach is compared with other well-developed multiobjective techniques. Experimental studies demonstrate that the present method can outperform other evolutionary-based solvers investigated in this paper with respect to convergence ability and distribution of the Pareto-optimal solutions. Therefore, the present evolutionary optimal control solver is more attractive and can offer an alternative for optimizing multi-objective continuous-time trajectory optimization problems.