CL-MAPF: Multi-Agent Path Finding for Car-Like robots with kinematic and spatiotemporal constraints

Wen, Licheng; Zhang, Zhen; Chen, Zhe; Zhao, Xiangrui; Liu, Yong

doi:10.1016/j.robot.2021.103997

Cited by 44 publications

(22 citation statements)

References 21 publications

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“…A key research topic involves successful navigation on the optimal path in an environment and various techniques are used to accomplish this. Wen et al use a set of four, four-wheeled, carlike robots capable of two-wheel drive and steering (2WD2WS) alongside a path planner known as the multi-agent pathfinding approach [8]. This method involves building a search tree to iteratively find a set of branches along the tree that provide an obstacle-free path to the goal.…”

Section: Path Planning Methodsmentioning

confidence: 99%

Untitled

2022

OJRAT

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With the continuous advancement of autonomous navigation in the automotive industry, a wide range of research is being conducted with the ultimate goal of improving or applying the autonomous navigation of passenger vehicles. However, the volume of research analyzing autonomous principles for multi-wheeled military and heavy vehicles that utilize steerable wheels is greatly lacking in comparison. These vehicles traditionally have three or more axles and are expected to carry larger loads in hazardous terrain resulting in increased complexity during modelling and analysis. As such the majority of research aimed toward steerable wheeled vehicles is performed in simulation. After a canvas of recent literature concerning the field of autonomous navigation experimentation, it was seen that the majority of studies are also performed using differential drive platforms with four or fewer wheels lacking car-like features such as actuated steering or suspension. Therefore, it is evident a gap exists in the area of physical experimentation for autonomous navigation of multi-wheeled platforms with steerable wheels. The proceeding sections of this paper will highlight recent, key studies and methods for this area concerning steerable, wheeled platforms.

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Section: Path Planning Methodsmentioning

confidence: 99%

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2022

OJRAT

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“…Dense Scenario 1) Description: In order to verify the feasibility and advantage of proposed hierarchical method in general scenarios, we conducted comparison experiments with CL-CBS proposed in [12]. The experiments were carried out in a 50m*38m map in which 4 vehicles, 6 vehicles and 8 vehicles were tested with and without obstacles in the middle of the map.…”

Section: B Comparison With Baseline Algorithms In the Challengingmentioning

confidence: 99%

Multi-Vehicle Coordinated Motion Planning Based on Interactive Primitive Tree

Wang

Zhang

et al. 2021

2021 IEEE International Conference on Unmanned Systems (ICUS)

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Multi-vehicle coordinated motion planning has always been challenged to safely and efficiently resolve conflicts under non-holonomic dynamic constraints. Constructing spatialtemporal corridors for multi-vehicle can decouple the highdimensional conflicts and further reduce the difficulty of obtaining feasible trajectories. Therefore, this paper proposes a novel hierarchical method based on interactive spatio-temporal corridors (ISTCs). In the first layer, based on the initial guidance trajectories, Mixed Integer Quadratic Programming is designed to construct ISTCs capable of resolving conflicts in generic multivehicle scenarios. And then in the second layer, Non-Linear Programming is settled to generate in-corridor trajectories that satisfy the vehicle dynamics. By introducing ISTCs, the multivehicle coordinated motion planning problem is able to be decoupled into single-vehicle trajectory optimization problems, which greatly decentralizes the computational pressure and has great potential for real-world applications. Besides, the proposed method searches for feasible solutions in the 3-D (x, y, t) configuration space, preserving more possibilities than the traditional velocity-path decoupling method. Simulated experiments in unsignalized intersection and challenging dense scenarios have been conduced to verify the feasibility and adaptability of the proposed framework.

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“…Licheng Wen [10] propose a novel hierarchical search-based solver called Carlike Conflict-Based Search to address this problem. It applies a body conflict tree to solve collisions considering shapes of the agents and introduces a new algorithm called Spatiotemporal Hybrid-State A* as the single-agent path planner to generate path satisfying both kinematic and spatiotemporal constraints.…”

Section: Introductionmentioning

confidence: 99%

Enhanced priority-based search for multi-agent path finding in roadway map

Chen¹,

Lü²,

Li³

2023

Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)

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Previous Multi-Agent Path Finding (MAPF) solvers rely on several simplifying assumptions. They consider the agents as holonomic robots and ignore the agent's size. There are two main directions of MAPF research. One is about MAPF's own shortcoming, how to improve the efficiency of the existing algorithm and the quality of the solution. The other direction is how to deal with diverse constraints brought by different cases when applying MAPF to practical problems. In this paper, the latter direction for solving the handling problem of forklifts in the roadway is more focused on. Firstly, we define MAPF for forklift robot which considers the agent's size and the topological paths as vertices. Secondly, we exploit the advantages of ECBS to make PBS runs faster with a bounded suboptimal solution. Then, we propose an acceleration strategy for collision detection, which is an important factor limiting the speed of the entire algorithm. Finally, we use the weighted low-level search to speed up calculation. The above series of improvements have enabled the MAPF algorithm to be applied in our real scenarios.

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CL-MAPF: Multi-Agent Path Finding for Car-Like robots with kinematic and spatiotemporal constraints

Cited by 44 publications

References 21 publications

Untitled

Untitled

Multi-Vehicle Coordinated Motion Planning Based on Interactive Primitive Tree

Enhanced priority-based search for multi-agent path finding in roadway map

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