A fast path planning by path graph optimization

Hwang, Jae Young; Kim, Jun Song; Lim, Sang Seok; Park, Kyu Ho

doi:10.1109/tsmca.2003.812599

Cited by 100 publications

(9 citation statements)

References 23 publications

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“…Similarly, lateral and longitudinal movements have been introduced within the steer relative coordinates to achieve optimal motion control (Werling et al, 2012). The global reference path is derived from the vision map using the lane-level accurate localization information via the LiDAR-based localization methods (Hwang et al, 2003;Li et al, 2017). In Li et al (2015), conjugate gradient nonlinear optimization and cubic spline curve are used to achieve a smooth global path from digital map and curvilinear coordinates framework is used to obtain optimal trajectories.…”

Section: Related Workmentioning

confidence: 99%

Path Planning of Mobile Robot With Improved Ant Colony Algorithm and MDP to Produce Smooth Trajectory in Grid-Based Environment

et al. 2020

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This approach has been derived mainly to improve quality and efficiency of global path planning for a mobile robot with unknown static obstacle avoidance features in grid-based environment. The quality of the global path in terms of smoothness, path consistency and safety can affect the autonomous behavior of a robot. In this paper, the efficiency of Ant Colony Optimization (ACO) algorithm has improved with additional assistance of A * Multi-Directional algorithm. In the first part, A * Multi-directional algorithm starts to search in map and stores the best nodes area between start and destination with optimal heuristic value and that area of nodes has been chosen for path search by ACO to avoid blind search at initial iterations. The path obtained in grid-based environment consist of points in Cartesian coordinates connected through line segments with sharp bends. Therefore, Markov Decision Process (MDP) trajectory evaluation model is introduced with a novel reward policy to filter and reduce the sharpness in global path generated in grid environment. With arc-length parameterization, a curvilinear smooth route has been generated among filtered waypoints and produces consistency and smoothness in the global path. To achieve a comfort drive and safety for robot, lateral and longitudinal control has been utilized to form a set of optimal trajectories along the reference route, as well as, minimizing total cost. The total cost includes curvature, lateral and longitudinal coordinates constraints. Additionally, for collision detection, at every step the set of optimal local trajectories have been checked for any unexpected obstacle. The results have been verified through simulations in MATLAB compared with previous global path planning algorithms to differentiate the efficiency and quality of derived approach in different constraint environments.

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Section: Related Workmentioning

confidence: 99%

Path Planning of Mobile Robot With Improved Ant Colony Algorithm and MDP to Produce Smooth Trajectory in Grid-Based Environment

et al. 2020

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“…For instance, the computer graphics community has widely adopted data structures that are based on or extensions of quad-trees (Finkel & Bentley, 1974;Samet, 1984), or higher-dimensional variants such as octrees (Orenstein, 1982), to achieve real-time graphics. Also the robotics and path planning communities have adopted multiscale data structures (Behnke, 2004;Hwang, Kim, Lim, & Park, 2003;Kambhampati & Davis, 1986;Lu et al, 2011). These algorithms usually represent an environment topologically or with an imposed grid and use some variant of Dijkstra's algorithm (Dijkstra, 1959) or A* (Hart, Nilsson, & Raphael, 1968) to determine an optimal path from start to target.…”

Section: 4mentioning

confidence: 99%

Transition Scale-Spaces: A Computational Theory for the Discretized Entorhinal Cortex

Waniek

2020

Neural Computation

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Although hippocampal grid cells are thought to be crucial for spatial navigation, their computational purpose remains disputed. Recently, they were proposed to represent spatial transitions and convey this knowledge downstream to place cells. However, a single scale of transitions is insufficient to plan long goal-directed sequences in behaviorally acceptable time. Here, a scale-space data structure is suggested to optimally accelerate retrievals from transition systems, called transition scale-space (TSS). Remaining exclusively on an algorithmic level, the scale increment is proved to be ideally [Formula: see text] for biologically plausible receptive fields. It is then argued that temporal buffering is necessary to learn the scale-space online. Next, two modes for retrieval of sequences from the TSS are presented: top down and bottom up. The two modes are evaluated in symbolic simulations (i.e., without biologically plausible spiking neurons). Additionally, a TSS is used for short-cut discovery in a simulated Morris water maze. Finally, the results are discussed in depth with respect to biological plausibility, and several testable predictions are derived. Moreover, relations to other grid cell models, multiresolution path planning, and scale-space theory are highlighted. Summarized, reward-free transition encoding is shown here, in a theoretical model, to be compatible with the observed discretization along the dorso-ventral axis of the medial entorhinal cortex. Because the theoretical model generalizes beyond navigation, the TSS is suggested to be a general-purpose cortical data structure for fast retrieval of sequences and relational knowledge. Source code for all simulations presented in this paper can be found at https://github.com/rochus/transitionscalespace .

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“…For instance, the computer graphics community has widely adopted data structures that are based on or extensions of quad-trees (Finkel and Bentley, 1974;Samet, 1984), or higher dimensional variants such as octrees (Orenstein, 1982), to achieve real-time graphics. Also the robotics and path planning communities have adopted multi-scale data structures (Behnke, 2004;Hwang et al, 2003;Kambhampati and Davis, 1986;Lu et al, 2011). These algorithms usually represent an environment topologically or with an imposed grid and use some variant of Dĳkstra's algorithm (W. Dĳkstra, 1959) or A* (Hart et al, 1968) to determine an optimal path from start to target.…”

Section: Relationship To Scale-spaces Data Structures Graph Algoritmentioning

confidence: 99%

Transition scale-spaces: A computational theory for the discretized entorhinal cortex

Waniek

2019

Preprint

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Although hippocampal grid cells are thought to be crucial for spatial navigation, their computational purpose remains disputed. Recently, they were proposed to represent spatial transitions and to convey this knowledge downstream to place cells. However, a single scale of transitions is insufficient to plan long goal-directed sequences in behaviorally acceptable time.Here, a scale-space data structure is suggested to optimally accelerate re-trievals from transition systems, called Transition Scale-Space (TSS). Remaining exclusively on an algorithmic level, the scale increment is proved to be ideally √ 2 for biologically plausible receptive fields. It is then argued that temporal buffering is necessary to learn the scale-space online. Next, two modes for retrieval of sequences from the TSS are presented, namely top-down and bottom-up. The two modes are evaluated in symbolic simulations, i.e., without biologically plausible spiking neurons. Additionally, a TSS is used for short-cut discovery in a simulated Morris water maze. Finally, the presented results are discussed in depth with respect to biological plausibility, and several testable predictions derived. Moreover, relations to other grid cell models, multi-resolution path planning, and scale-space theory are highlighted. Summarized, reward-free transition encoding is shown here, in a theoretical model, to be compatible with the observed discretization along the dorso-ventral axis of medial Entorhinal Cortex (MEC).Because the theoretical model generalizes beyond navigation, the TSS is suggested to be a general-purpose cortical data structure for fast retrieval of sequences and relational knowledge.

show abstract

A fast path planning by path graph optimization

Cited by 100 publications

References 23 publications

Path Planning of Mobile Robot With Improved Ant Colony Algorithm and MDP to Produce Smooth Trajectory in Grid-Based Environment

Path Planning of Mobile Robot With Improved Ant Colony Algorithm and MDP to Produce Smooth Trajectory in Grid-Based Environment

Transition Scale-Spaces: A Computational Theory for the Discretized Entorhinal Cortex

Transition scale-spaces: A computational theory for the discretized entorhinal cortex

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