Python Implementation of multi-vehicle motion planning, including our proposed method, namely postured collision avoidance (PCA), the reciprocal volocity obstacles (RVO), and the optimal reciprocal collision avoidance (ORCA, also namely RVO2) .
This paper investigates more practical applications in the real world for non-holonomic unmanned ground vehicles. In this case, a strategy of diversion is designed to optimize the smoothness of motion. Considering the problem of the posture constraints, a postured collision avoidance (PCA) algorithm is proposed for the motion planning of the multiple non-holonomic unmanned ground vehicles. In addition, we provide the optimal reciprocal collision avoidance (ORCA) method and reciprocal velocity obstacles (RVO) method for comparison.
Paper: Multi-Vehicle Motion Planning with Posture Constraints in Real World, Gang Xu, Yansong Chen, Junjie Cao*, Deye Zhu, Weiwei Liu, and Yong Liu*, Accepted as TMECH/AIM Focused Section Paper in IEEE-ASME Transactions on Mechatronics (TMECH).
pip install numpy
pip install pandas
pip install matplotlibcd run_py
python run_pca.py
For the test, you can select the scenarios, including circle, random.
In the first simulation, the preferred speed is 0.22 m/s. In the compared experiments, the preferred speed is 1.0 m/s. All agents' radius is 0.2 m. The radius of circle benchmark is set as 15 m for DRVO, ORCA and RVO. The square of random benchmark is set as 30 m * 30 m for DRVO, ORCA and RVO. The radius of circle benchmark is set as 40 m for PCA and RVO + Dubins. The square of random benchmark is set as 80 m * 80 m for PCA and RVO + Dubins.
In the large scale, the radius of circle benchmark is set as 20 m for DRVO, ORCA and RVO, the square of random benchmark is set as 60 m * 60 m for DRVO, ORCA and RVO, the radius of circle benchmark is set as 50 m for PCA and RVO + Dubins, and the square of random benchmark is set as 100 m * 100 m for PCA and RVO + Dubins.