Hierarchical rough terrain motion planning using an optimal sampling-based method

Brunner, Michael; Brüggemann, Bernd; Schulz, Dirk

doi:10.1109/icra.2013.6631372

Cited by 63 publications

(24 citation statements)

References 12 publications

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“…Other combinations involve a stochastic discrete stage, like rapidly-exploring random trees (RRT, RRT*) (see, e.g., [42]) or Probabilistic Roadmaps (PRMs) [43] in combination with a second NLP stage. Another approach was taken in [44], where the authors use a combination of A * and RRT* to find an optimal trajectory. These algorithms reveal remarkable performance when it comes to obstacle avoidance.…”

Section: Discopter Algorithmmentioning

confidence: 99%

A Discrete-Continuous Algorithm for Free Flight Planning

2020

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We propose a hybrid discrete-continuous algorithm for flight planning in free flight airspaces. In a first step, our discrete-continuous optimization for enhanced resolution (DisCOptER) method computes a globally optimal approximate flight path on a discretization of the problem using the A* method. This route initializes a Newton method that converges rapidly to the smooth optimum in a second step. The correctness, accuracy, and complexity of the method are governed by the choice of the crossover point that determines the coarseness of the discretization. We analyze the optimal choice of the crossover point and demonstrate the asymtotic superority of DisCOptER over a purely discrete approach.

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Section: Discopter Algorithmmentioning

confidence: 99%

A Discrete-Continuous Algorithm for Free Flight Planning

2020

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“…A family of more modern algorithms is the RRT * or rapidly-exploring random tree family [16]. The two-phase approach of A * − RRT * [17] first performs a coarse graphsearch which is used to direct the RRT * search. Using this group of methods from the end configuration the tree can be reused for different starting locations, however, our proposed method can reuse parts of the calculated field even if both start and end configurations are changed.…”

Section: A Related Workmentioning

confidence: 99%

Computing a Collision-Free Path Using the Monogenic Scale Space

Holmquist

Senel

Felsberg

2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Mobile robots have been used for various purposes with different functionalities which require them to freely move in environments containing both static and dynamic obstacles to accomplish given tasks. One of the most relevant capabilities in terms of navigating a mobile robot in such an environment is to find a safe path to a goal position. This paper shows that there exists an accurate solution to the Laplace equation which allows finding a collision-free path and that it can be efficiently calculated for a rectangular bounded domain such as a map which is represented as an image. This is accomplished by the use of the monogenic scale space resulting in a vector field which describes the attracting and repelling forces from the obstacles and the goal. The method is shown to work in reasonably convex domains and by the use of tessellation of the environment map for non-convex environments.

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“…Later, the optimized variant of B-RRT [41] was also proposed. A * -RRT [42] is a two-phase method, in the first phase, the algorithm searches in a lower dimension, and in the second phase it works as RRT * to search the path in higher-dimension. Furthermore, RRT X , [43] is another extensions of RRT * for the path planning in a dynamic environment.…”

Section: Introductionmentioning

confidence: 99%

Control Framework for Trajectory Planning of Soft Manipulator Using Optimized RRT Algorithm

Khan

Kadry

et al. 2020

IEEE Access

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This paper proposes a model-free control framework for the path planning of the rigid and soft robotic manipulator using an intelligent algorithm called Weighted Jacobian Rapidly-exploring Random Tree (WJRRT). The optimization approach is used to model the path planning problem, which is independent of the robotic model, and then used the WJRRT algorithm to solve it. WJRRT algorithm not only explores the cartesian space for the end-effector of the robotic manipulator randomly but also directs it towards the goal-position when required. It is robust enough to tackle the uncertainties in the manipulator and make the computation of path planning more efficient. WJRRT assigned a fitness value to each node of the tree. Based on the fitness values algorithm computes the final path, which is a trade-off between efficiency and safety of the path. The simulation results of two, three, and seven degrees of freedom (DOF) robotic manipulators are presented and compared with JT-RRT, Bi-RRT, and TB-RRT algorithms. Experimental results are verified using a soft manipulator made from flexible materials, i.e., polypropylene and polychloroprene. Their flexible structure makes their control complex and creates uncertainties in the model. The simulation and experimental results demonstrate that WJRRT can efficiently and accurately control the motion of manipulators.

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Hierarchical rough terrain motion planning using an optimal sampling-based method

Cited by 63 publications

References 12 publications

A Discrete-Continuous Algorithm for Free Flight Planning

A Discrete-Continuous Algorithm for Free Flight Planning

Computing a Collision-Free Path Using the Monogenic Scale Space

Control Framework for Trajectory Planning of Soft Manipulator Using Optimized RRT Algorithm

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