This is the repository of Reactive Driving Support (RDS), a method for robots to avoid imminent collisions with moving obstacles, which has been developed as part of research at LASA, EPFL. This accompanying short video explains the method. The corresponding publication is under review. This work was funded by the EU H2020 project Crowdbot.
The repository hosts code that implements RDS, simulates and evaluates navigation with RDS in a crowd of pedestrians, and provides an interface for executing RDS on a robot using ROS. The original code in this repository (everything except the following third parties' contributions) is subject to the MIT LICENSE.
The folder spline contains the spline library from this project which is subject to the GPL 2 license. Further, the folder rds/data contains pedestrian trajectory data from here. Finally, the folder RVO2 contains a modified version of the RVO2 library which is subject to the Apache 2 license.
RDS has been developed for non-holonomic robots whose shape is bounded by a capsule (i.e. a circle swept along a line segment). However, a wrapper is available which allows to use RDS for holonomic robots whose shape is bounded by a circle. In this case, RDS is very similar to using finite-horizon Velocity Obstacles for avoiding collisions. Integration of RDS in a robotic system is implemented via ROS. The following picture shows the connection of RDS to system components for sensing (laserscanners and/or people tracker) and high-level planning (client node of RDS).
RDS obtains information about obstacles by listening to up to two laserscanners under the topics "front_lidar/scan" and "rear_lidar/scan" and a people tracker under the topic "rwth_tracker/tracked_persons" (unless this build option is disabled). The messages by perception components must refer to a frame which RDS can relate to its own frame, which is termed "main_body_frame". For non-holonomic robots, this frame's origin is the midpoint of the wheel axle and its y-axis points forward and the x-axis is aligned with the wheel axle. For holonomic robots, this frame's origin is the robot's center (as in the picture below). If the messages from perception components refer to different frames, the transformation between their frames and the "main_body_frame" must be provided e.g. by a static transform publisher.
The procedure for building all the shown components is given in this and this section below. A demo and explanations are given in this this section for using RDS through ROS in its standard way with non-holonomic capsule-shaped robots.
The package rds_ros contains the wrapper wrapper_holonomic.hpp, which handles calling RDS for holonomic robots. The package user_pkg_example uses the holonomic wrapper of RDS and is included here in the repository. It is a minimal example for a package that calls the rds_ros package for holonomic robots and is intended to demonstrate how to call RDS from within any external package in a similar way. The package user_pkg_example is built when following the general instructions for building this repository. It can be tested afterwards via
roslaunch user_pkg_example demo_holonomic_standalone.launch
In general, the following applies. A program can use the holonomic wrapper by including the respective header. Building requires adding the package rds_ros to its CMakeLists.txt. For launching such a program, its launch file must simply include the file rds_holonomic.launch from the package rds_ros, which will launch the nodes that execute RDS behind the wrapper.
The holonomic wrapper is configured by a file (here in user_pkg_example) which it loads upon creation and which specifies the radius of the bounding circle, the robot's limits of velocity and acceleration (with respect to the frame "main_body_frame"), and other parameters. The following picture explains the configuration file of the holonomic wrapper and the associated definitions.
RDS assumes that the frame "main_body_frame" defines the center for the bounding circle, which RDS uses to approximate the robot's shape. Further, the velocity commands which are sent to and received from RDS describe the origin center's velocity by the cartesian components with respect to the frame's axes.
The software's structure is as in the following picture.
Instructions follow to set up and run RDS and some demos in Ubuntu 16.04. Below, the first section is for simulations, and the second section is for the ROS interface. First, the following steps are necessary in both cases.
This installs the graphics library SDL2, which is a prerequisite.
sudo apt install libsdl2-dev
This gets the code and builds the basic components for RDS.
git clone https://github.com/epfl-lasa/rds.git
cd rds/RVO2
mkdir build
cd build
cmake ..
make
sudo make install
cd ../../rds
make
The previous steps built the executable baseline_crowd_rds_5 under [THIS_REPOSITORY_ROOT_FOLDER]/rds/build/. The executable is for simulating a robot using RDS in a crowd whose individual pedestrians track reference trajectories from a real-world dataset.
For running the executable, one needs to be in the right folder:
cd [THIS_REPOSITORY_ROOT_FOLDER]/rds
The following command runs three simulations, one where there is no robot and two where the robot replaces the 7th pedestrian and tracks its reference trajectory, once using ORCA and once using RDS.
./build/baseline_crowd_rds_5 7
One can replace 7 by any integer i with 0 <= i < 430.
The following command runs the simulations for all configurations (with a gui) and saves the results of the evaluation as metrics_evaluation.csv.
./build/baseline_crowd_rds_5 gui
The following command runs the simulations for all configurations (without a gui) and saves the results of the evaluation as metrics_evaluation.csv.
./build/baseline_crowd_rds_5
RDS can be integrated with a robot's perception and control system by using ROS and the nodes from this repository. The following sections show how to build and use these nodes in a ROS environment.
As prerequisites, one needs to install ROS Kinetic and the catkin command line tools as the links describe. The next steps (below) are to create a catkin workspace and to build there this repository's nodes.
mkdir -p catkin_ws_rds/src
cd catkin_ws_rds/src
ln -s [THIS_REPOSITORY_ROOT_FOLDER] rds
cd ..
catkin init
catkin build --cmake-args -DRDS_ROS_USE_TRACKER=OFF
For integrating the people tracker of Crowdbot, the package frame_msgs must be in the catkin workspace. The node for RDS can then listen to frame_msgs::TrackedPersons messages and treat the detections as obstacles. The following command builds the node accordingly (requires the external package frame_msgs).
catkin build
The above steps have built the executables of the following four nodes.
The rds_ros_node offers a service which execute RDS, listens to laserscan (and optionally tracker) messages, and publishes its operations as GUI messages.
The rds_gui_ros_node executes a GUI, listens to GUI messages and displays them.
The rds_ros_nominal_command_node calls the service which executes RDS from the rds_ros_node. It calls this service using a constant nominal command and is just for demonstration and debugging purposes.
The rds_ros_fake_lrf_node publishes two fake laserscan messages representing a front and rear sensor. It is just for demonstration and debugging purposes.
The following commands run the nodes together and demonstrate the operation of RDS in ROS.
cd catkin_ws_rds
. devel/setup.bash
roslaunch rds_ros demo_standalone.launch
Using RDS via the ROS interface means sending a request for the service which the rds_ros_node offers (as done by this node in python and this node in C++ from the demo). The file VelocityCommandCorrectionRDS.srv defines this service's layout in terms of the parameters which the service's request and its response specify. The request's parameters are passed as arguments to RDS, whereas the response's parameters store the results which RDS computes. The list below explains each parameters' meaning for RDS.
The request's parameter nominal_command specifies the nominal linear and angular velocity command which one issues for the robot but which does not yet take into account obstacles.
The robot's footprint is bounded by a capsule for RDS, whose geometry is specified by the parameters capsule_center_front_y, capsule_center_rear_y and capsule_radius. The reference_point_y denotes the point whose velocity is optimized to minimize the absolute difference from the point's nominal velocity. The definitions are according to the following sketch.
The parameter rds_tau specifies the time horizon which RDS uses to construct velocity obstacles.
The parameter rds_delta specifies the margin which RDS adds to each obstacle's radius to be more conservative.
The following sketch explains the parameters that specify constant limits for the linear and angular velocity (with their names on the right).
The parameters acc_limit_linear_abs_max and acc_limit_angular_abs_max specify the linear and angular acceleration limits, respectively. To enforce them, the rds_ros_node relies on the next parameter dt, which should specify the time i.e. period between sequential requests. Then, the rds_ros_node converts the acceleration limits into velocity limits that are centered around the velocity which RDS computed in the previous cycle and enclose the velocities which are reachable within dt while satisfying the specified acceleration limits.
The boolean parameter lrf_point_obstacles specifies whether to take laserscan points directly into account as obstacles or not (which is the only way for RDS to perceive obstacles without a tracker).
The boolean parameter ORCA_implementation specifies whether to use an implementation of ORCA instead of RDS (for comparison purposes).
The response's parameter corrected_command reports the command which RDS has computed.
The response's parameter call_counter simply counts how often the service of the rds_ros_node has been called since the node was launched.