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img_env

这是中科大实验室基于grid map的导航仿真环境, 适配gym的api

  • Very fast, based on C++, customized for robot navigation.
  • Accurate, not perform physical simulation and time simulation.
  • Parallelization，support multi-scene and multi-robot.
  • Pedestrian simulation，contains pedestrian simulation based on Pedsim and ORCA.
  • Gym API, easy to use in conjunction with other reinforcement learning frameworks.
  • Grid_map output， directly outputs grid_map or raw laser data.
  • Parameterized configuration, easily configure environment information via yaml file, including robot shape and size, pedestrian strategy, environmental map, and more.
  • Very lightweight, only depends on OpenCV for visualization. 200 robots and 200 obstacles have been simulated.

img_env

Quick Start

推荐系统环境：Ubuntu20 + ROS noetic + python3

sudo apt-get install libedit-dev

git clone [email protected]:DRL-Navigation/img_env.git

cd img_env

编译:

catkin_make --only-pkg-with-deps img_env

添加依赖

echo "source `pwd`/devel/setup.bash" >> ~/.bashrc

配置yaml

进入envs/cfg 对要跑的yaml进行配置， 如果当前配置要开几个一起跑，设置env_num参数。

生成一个launch文件

可以手写,也可以用create_launch.py脚本, 当你 ROS node比较多的时候,建议用脚本, 用法如下:

python create_launch.py test envs/cfg/test.yaml

第一个参数为task name（也是生成的launch文件名），之后的参数为yaml文件， 如果要跑好几个yaml环境，

python create_launch.py test A.yaml B.yaml C.yaml

生成的launch文件位于src/img_env/tmp下

启动ROS

新开一个终端

roslaunch img_env test.launch

跑python

python env_test.py

说明

State 状态类

envs/state/state.py 以下所有值类型为numpy， 均为env下的所有robots的属性， 即第一个维度为robot_num

    vector_states,  [robot_num, state_dim] 向量形式的状态，state_dim=3时候，分别表示距离终点x距离 y距离和yaw
    sensor_maps, [robot_num, batch, 48, 48] 以robot为中心的激光图
    is_collisions, [robot_num] 判断碰撞，0表示没撞,1撞障碍物,2撞行人 ,3撞别的小车
    is_arrives, [robot_num] 判断到达，0表示没达到，1到达
    lasers, [robot_num, laser_range_total] 一维原始激光数据
    ped_vector_states, [robot_num, n] 一维行人向量信息，维度n = 1 + vector_number * max_ped_number, 开头的1表示实际ped number
    ped_maps, [robot_num, 3, 48, 48]三通道行人图，分别表示行人x速度，y速度以及位置
    step_ds, [robot_num] 每一步靠近target的距离
    ped_min_dists, [robot_num] 一个值，最近行人距离

Wrapper

修改reward, 动作空间等操作, 请依照gym的规范, 进行wrapper封装

!!! NOTE: 在yaml里面填入wrapper的顺序非常重要, 第一个封装在最里层, 最先执行,最后一个封装在最外层, 最后执行,这里不明白的想象一下树遍历的先序和后序

环境参数

当前, 所有参数设置均在yaml里, 由python读取后, 发给C++

GUI 运行期间如何关闭打开

参数服务器

#关闭
rosparam set test/show_gui false
#开启
rosparam set test/show_gui true
#注意这里的test改成你想开启的node name， 具体为yaml里设置的env_name+env_id
#可通过rosnode list 查看所有node

行人模型

行人控制方法分为 pedsim, rvo , ervo, 代码均为开源, 已下载并放在3rd_party

rvo为orca控制, ervo 为情感类(emitional)的rvo , 在rvo的基础上进行了一些修改, 具体可见ervo代码

修改行人模型在yaml文件里, ped_sim:type 下面可选 pedscene,rvoscene,ervoscene

请引用我们的相关论文：

@Article{chen2020distributed,
title = {Distributed Non-Communicating Multi-Robot Collision Avoidance via Map-Based Deep Reinforcement Learning},
author = {Chen, Guangda and Yao, Shunyi and Ma, Jun and Pan, Lifan and Chen, Yu'an and Xu, Pei and Ji, Jianmin and Chen, Xiaoping},
journal = {Sensors},
volume = {20},
number = {17},
pages = {4836},
year = {2020},
publisher = {Multidisciplinary Digital Publishing Institute},
doi = {10.3390/s20174836},
url = {https://www.mdpi.com/1424-8220/20/17/4836}
};

@inproceedings{yao2021crowd,
  title={Crowd-Aware Robot Navigation for Pedestrians with Multiple Collision Avoidance Strategies via Map-based Deep Reinforcement Learning},
  author={Yao, Shunyi and Chen, Guangda and Qiu, Quecheng and Ma, Jun and Chen, Xiaoping and Ji, Jianmin},
  booktitle={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={8144--8150},
  year={2021},
  organization={IEEE}
}