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Search-Based Path Planning Algorithm for Autonomous Parking:Multi-Heuristic Hybrid A*

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This paper proposed a novel method for autonomous parking. Autonomous parking has received a lot of attention because of its convenience, but due to the complex environment and the non-holonomic constraints of vehicle, it is difficult to get a collision-free and feasible path in a short time. To solve this problem, this paper introduced a novel algorithm called Multi-Heuristic Hybrid A* (MHHA*) which incorporates the characteristic of Multi-Heuristic A* and Hybrid A*. So it could provide the guarantee for completeness, the avoidance of local minimum and sub-optimality, and generate a feasible path in a short time. And this paper also proposed a new collision check method based on coordinate transformation which could improve the computational efficiency. The performance of the proposed method was compared with Hybrid A* in simulation experiments and its superiority has been proved.

Jihao Huang, Zhitao Liu, Xuemin Chi, Feng Hong, Hongye Su• 2022

Related benchmarks

TaskDatasetResultRank
Autonomous ParkingParking Tasks Complex Parallel
Minimum Time (T)0.0298
9
Autonomous ParkingEasy Difficulty Parking Scenarios Parallel
Minimum Parking Time0.032
9
Autonomous Parking Path PlanningExtreme Difficulty Parking Parallel
Minimum Time0.0902
9
Autonomous ParkingParking Tasks Complex Reverse
Min Time0.0558
8
Autonomous ParkingEasy Difficulty Parking Scenarios Forward
Min Time0.0345
8
Autonomous ParkingEasy Difficulty Parking Scenarios Reverse
Min(T)0.0477
8
Autonomous Parking Path PlanningExtreme Difficulty Parking Forward
Min Time0.0366
8
Autonomous ParkingParking Tasks Complex Forward
Min Time0.037
8
Autonomous Parking Path PlanningDifficulty Parking Reverse (Extreme)
Min Time0.146
8
Parking Path PlanningParking Scenario a
Failure Rate0.00e+0
3
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