Search-Based Path Planning Algorithm for Autonomous Parking:Multi-Heuristic Hybrid A*
About
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.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Autonomous Parking | Parking Tasks Complex Parallel | Minimum Time (T)0.0298 | 9 | |
| Autonomous Parking | Easy Difficulty Parking Scenarios Parallel | Minimum Parking Time0.032 | 9 | |
| Autonomous Parking Path Planning | Extreme Difficulty Parking Parallel | Minimum Time0.0902 | 9 | |
| Autonomous Parking | Parking Tasks Complex Reverse | Min Time0.0558 | 8 | |
| Autonomous Parking | Easy Difficulty Parking Scenarios Forward | Min Time0.0345 | 8 | |
| Autonomous Parking | Easy Difficulty Parking Scenarios Reverse | Min(T)0.0477 | 8 | |
| Autonomous Parking Path Planning | Extreme Difficulty Parking Forward | Min Time0.0366 | 8 | |
| Autonomous Parking | Parking Tasks Complex Forward | Min Time0.037 | 8 | |
| Autonomous Parking Path Planning | Difficulty Parking Reverse (Extreme) | Min Time0.146 | 8 | |
| Parking Path Planning | Parking Scenario a | Failure Rate0.00e+0 | 3 |