Energy-Optimal Motion Planning for Multiple Robotic Vehicles With Collision Avoidance

Häusler, Andreas J.; Saccon, Alessandro; Aguiar, A. Pedro; Hauser, John; Pascoal, António

doi:10.1109/tcst.2015.2475399

Cited by 65 publications

(29 citation statements)

References 30 publications

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“…The minimum-time problem (6) is reformulated in the new (arc-length dependent) variablesx w andū, by using the cost (29), the transverse dynamics (25) (39)…”

Section: Equivalent Minimum-time Formulation and Optimal Control Smentioning

confidence: 99%

“…While in [24] the maximum velocity profile (for a given path) is computed for a motorcycle model by using a quasi-static approximation of the dynamics, we optimize the whole state-input trajectory and we consider the full nonlinear dynamics of the quadrotor. Finally, other optimization strategies using the PRONTO method are [25] and [26], which aim to compute respectively minimum-energy trajectories for two-wheeled mobile robots and minimumdistance trajectories (from an unfeasible desired maneuver) for UAVs. Differently from these works, we consider a more general three-dimensional space with position constraints and we reformulate the minimum-time problem by using the transverse coordinates.…”

Section: Introductionmentioning

confidence: 99%

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Minimum-Time Trajectory Generation for Quadrotors in Constrained Environments

Spedicato

Notarstefano

2018

IEEE Trans. Contr. Syst. Technol.

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Abstract-In this paper, we present a novel strategy to compute minimum-time trajectories for quadrotors in constrained environments. In particular, we consider the motion in a given flying region with obstacles and take into account the physical limitations of the vehicle. Instead of approaching the optimization problem in its standard time-parameterized formulation, the proposed strategy is based on an appealing re-formulation. Transverse coordinates, expressing the distance from a frame path, are used to parameterize the vehicle position and a spatial parameter is used as independent variable. This re-formulation allows us to (i) obtain a fixed horizon problem and (ii) easily formulate (fairly complex) position constraints. The effectiveness of the proposed strategy is proven by numerical computations on two different illustrative scenarios. Moreover, the optimal trajectory generated in the second scenario is experimentally executed with a real nano-quadrotor in order to show its feasibility.

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“…The minimum-time problem (6) is reformulated in the new (arc-length dependent) variablesx w andū, by using the cost (29), the transverse dynamics (25) (39)…”

Section: Equivalent Minimum-time Formulation and Optimal Control Smentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Minimum-Time Trajectory Generation for Quadrotors in Constrained Environments

Spedicato

Notarstefano

2018

IEEE Trans. Contr. Syst. Technol.

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“…Generally, this type of problem aims to find the optimal state and control sequences so as to optimize the predefined performance index. Relative works on this topic can be found in various scientific and engineering applications such as agent/robot trajectory planning [3], [4], autonomous vehicle optimal path design [5], and spacecraft optimal control systems [6]- [8]. More precisely, in [2] the author proposed a time-optimal trajectory generation strategy for a multi-body car model.…”

Section: Introductionmentioning

confidence: 99%

Solving Multiobjective Constrained Trajectory Optimization Problem by an Extended Evolutionary Algorithm

Chai

Tsourdos

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.

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“…In Joordens et al 1 and Champion et al, 2 a complete review of the latest cooperative strategies in the underwater field has been described. In Hausler et al, 8 an interesting method for the optimal motion planning of vehicles is presented.…”

Section: Introductionmentioning

confidence: 99%

Optimization of potential field method parameters through networks for swarm cooperative manipulation tasks

Furferi

Conti

Meli

et al. 2016

International Journal of Advanced Robotic Systems

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An interesting current research field related to autonomous robots is mobile manipulation performed by cooperating robots (in terrestrial, aerial and underwater environments). Focusing on the underwater scenario, cooperative manipulation of Intervention-Autonomous Underwater Vehicles (I-AUVs) is a complex and difficult application compared with the terrestrial or aerial ones because of many technical issues, such as underwater localization and limited communication.A decentralized approach for cooperative mobile manipulation of I-AUVs based on Artificial Neural Networks (ANNs) is proposed in this article. This strategy exploits the potential field method; a multi-layer control structure is developed to manage the coordination of the swarm, the guidance and navigation of I-AUVs and the manipulation task. In the article, this new strategy has been implemented in the simulation environment, simulating the transportation of an object. This object is moved along a desired trajectory in an unknown environment and it is transported by four underwater mobile robots, each one provided with a seven-degrees-of-freedom robotic arm. The simulation results are optimized thanks to the ANNs used for the potentials tuning.

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Energy-Optimal Motion Planning for Multiple Robotic Vehicles With Collision Avoidance

Cited by 65 publications

References 30 publications

Minimum-Time Trajectory Generation for Quadrotors in Constrained Environments

Minimum-Time Trajectory Generation for Quadrotors in Constrained Environments

Solving Multiobjective Constrained Trajectory Optimization Problem by an Extended Evolutionary Algorithm

Optimization of potential field method parameters through networks for swarm cooperative manipulation tasks

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