Mobile Robot Motion Planning in Multi-Resolution Lattices with Hybrid Dimensionality

Petereit, Janko; Emter, Thomas; Frey, Christian W.

doi:10.3182/20130626-3-au-2035.00018

Cited by 22 publications

(32 citation statements)

References 14 publications

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“…Lattice-based motion planning framework also been extended to plan in both structured [11] and unstructured dynamic environments [1]. It has also been used off-road in [12], which employs a multi-resolution state lattice with a lattice graph of high resolution in the vicinity of the vehicle, but time and dynamical obstacles are not considered.…”

Section: A Related Workmentioning

confidence: 99%

“…In [1], time is included in the discretization of the lattice graph, however, rather than using an optimizationbased approach for motion primitive generation, the motion primitives are generated via constant control signals and a massive number of simulations of the system. This method for generating motion primitives is not suitable for systems with complex dynamics, especially not unstable systems.…”

Section: A Related Workmentioning

confidence: 99%

“…We propose a principled solution to this problem by using a unified optimization-based motion planning and control architecture, where both layers use the system dynamics to generate and execute feasible trajectories in real-time. In particular, we present a novel receding-horizon motion planner which has similarities to multi-resolution state lattices [1], [2]. Motion primitives, a set of dynamically feasible trajectories, are generated offline by the use of numerical optimal control.…”

Section: Introductionmentioning

confidence: 99%

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Receding-Horizon Lattice-Based Motion Planning with Dynamic Obstacle Avoidance

Andersson

Ljungqvist

Tiger

et al. 2018

2018 IEEE Conference on Decision and Control (CDC)

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A key requirement of autonomous vehicles is the capability to safely navigate in their environment. However, outside of controlled environments, safe navigation is a very difficult problem. In particular, the real-world often contains both complex 3D structure, and dynamic obstacles such as people or other vehicles. Dynamic obstacles are particularly challenging, as a principled solution requires planning trajectories with regard to both vehicle dynamics, and the motion of the obstacles. Additionally, the real-time requirements imposed by obstacle motion, coupled with real-world computational limitations, make classical optimality and completeness guarantees difficult to satisfy. We present a unified optimizationbased motion planning and control solution, that can navigate in the presence of both static and dynamic obstacles. By combining optimal and receding-horizon control, with temporal multi-resolution lattices, we can precompute optimal motion primitives, and allow real-time planning of physically-feasible trajectories in complex environments with dynamic obstacles. We demonstrate the framework by solving difficult indoor 3D quadcopter navigation scenarios, where it is necessary to plan in time. Including waiting on, and taking detours around, the motions of other people and quadcopters.

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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Receding-Horizon Lattice-Based Motion Planning with Dynamic Obstacle Avoidance

Andersson

Ljungqvist

Tiger

et al. 2018

2018 IEEE Conference on Decision and Control (CDC)

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“…These methods variegate the resolution of the workspace, configuration space, time-axis, or a combination thereof. [32][33][34][35][36][37] However, to the best of the authors' knowledge, this is the first paper to use fractals as the multi-resolution building block for robot motion planning.…”

Section: Fractals For Optimal Path Generationmentioning

confidence: 99%

Minimally actuated hyper-redundant robots: Motion planning methods based on fractals and self-organizing systems

Mann

Damti

Zarrouk

2019

International Journal of Advanced Robotic Systems

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This article presents a motion planning method for a novel hyper-redundant robot with minimal actuation based on the principles of fractals and self-organizing systems. The robot consists of multiple links connected by passive joints and a movable actuator. The actuator travels over the links to a given joint and adjusts the relative angle between the two adjacent links allowing the robot to undergo the same wide range of motions of hyper-redundant robot but with only one actuator. A suitable objective of the motion planner is to minimize the number of actuator traversals, which translates into minimizing the number of bends in the c-space trajectory. To this end, we propose a novel method for motion planning using fractals and self-organizing systems. A self-similar pattern for the path is implemented to map a path from start to finish. Each iteration of path segments is of smaller dimension than the previous one and is appended to it, just as in classical fractals. This process continues until a feasible trajectory is calculated. Self-organizing systems are then applied to this trajectory post-processing to optimize it by eliminating bends in the path. Examples of the robot maneuvering around obstacles and through confined spaces are shown to demonstrate the efficacy of the motion planner.

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“…To achieve further planning acceleration, it is an obvious idea to combine multiresolution and multidimensional planning. However, only few works, such as by Petereit et al [15] address this. Different planning dimensionalities and resolutions are applied by using different sets of motion primitives.…”

Section: Related Workmentioning

confidence: 99%

Planning Hybrid Driving-Stepping Locomotion on Multiple Levels of Abstraction

Klamt

Behnke

2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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Navigating in search and rescue environments is challenging, since a variety of terrains has to be considered. Hybrid driving-stepping locomotion, as provided by our robot Momaro, is a promising approach. Similar to other locomotion methods, it incorporates many degrees of freedom-offering high flexibility but making planning computationally expensive for larger environments.We propose a navigation planning method, which unifies different levels of representation in a single planner. In the vicinity of the robot, it provides plans with a fine resolution and a high robot state dimensionality. With increasing distance from the robot, plans become coarser and the robot state dimensionality decreases. We compensate this loss of information by enriching coarser representations with additional semantics. Experiments show that the proposed planner provides plans for large, challenging scenarios in feasible time.

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Mobile Robot Motion Planning in Multi-Resolution Lattices with Hybrid Dimensionality

Cited by 22 publications

References 14 publications

Receding-Horizon Lattice-Based Motion Planning with Dynamic Obstacle Avoidance

Receding-Horizon Lattice-Based Motion Planning with Dynamic Obstacle Avoidance

Minimally actuated hyper-redundant robots: Motion planning methods based on fractals and self-organizing systems

Planning Hybrid Driving-Stepping Locomotion on Multiple Levels of Abstraction

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