WIP: plot based simulation
mcarfagno
parent
60f028e25b
commit
13935940b4
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@ -1,208 +0,0 @@
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cmake_minimum_required(VERSION 2.8.3)
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project(husky_mpc)
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## Compile as C++11, supported in ROS Kinetic and newer
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# add_compile_options(-std=c++11)
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## Find catkin macros and libraries
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## if COMPONENTS list like find_package(catkin REQUIRED COMPONENTS xyz)
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## is used, also find other catkin packages
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find_package(catkin REQUIRED COMPONENTS
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geometry_msgs
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nav_msgs
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roscpp
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rospy
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std_msgs
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)
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## System dependencies are found with CMake's conventions
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# find_package(Boost REQUIRED COMPONENTS system)
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## Uncomment this if the package has a setup.py. This macro ensures
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## modules and global scripts declared therein get installed
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## See http://ros.org/doc/api/catkin/html/user_guide/setup_dot_py.html
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# catkin_python_setup()
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################################################
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## Declare ROS messages, services and actions ##
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################################################
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|
||||
## To declare and build messages, services or actions from within this
|
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## package, follow these steps:
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## * Let MSG_DEP_SET be the set of packages whose message types you use in
|
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## your messages/services/actions (e.g. std_msgs, actionlib_msgs, ...).
|
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## * In the file package.xml:
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## * add a build_depend tag for "message_generation"
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## * add a build_depend and a exec_depend tag for each package in MSG_DEP_SET
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## * If MSG_DEP_SET isn't empty the following dependency has been pulled in
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## but can be declared for certainty nonetheless:
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## * add a exec_depend tag for "message_runtime"
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||||
## * In this file (CMakeLists.txt):
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## * add "message_generation" and every package in MSG_DEP_SET to
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## find_package(catkin REQUIRED COMPONENTS ...)
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## * add "message_runtime" and every package in MSG_DEP_SET to
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## catkin_package(CATKIN_DEPENDS ...)
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## * uncomment the add_*_files sections below as needed
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## and list every .msg/.srv/.action file to be processed
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## * uncomment the generate_messages entry below
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## * add every package in MSG_DEP_SET to generate_messages(DEPENDENCIES ...)
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## Generate messages in the 'msg' folder
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# add_message_files(
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# FILES
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# Message1.msg
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# Message2.msg
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# )
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## Generate services in the 'srv' folder
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# add_service_files(
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# FILES
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# Service1.srv
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# Service2.srv
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# )
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## Generate actions in the 'action' folder
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# add_action_files(
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# FILES
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# Action1.action
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# Action2.action
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# )
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## Generate added messages and services with any dependencies listed here
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# generate_messages(
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# DEPENDENCIES
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# geometry_msgs# navigation_msgs# std_msgs
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# )
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################################################
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## Declare ROS dynamic reconfigure parameters ##
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################################################
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|
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## To declare and build dynamic reconfigure parameters within this
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## package, follow these steps:
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## * In the file package.xml:
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## * add a build_depend and a exec_depend tag for "dynamic_reconfigure"
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## * In this file (CMakeLists.txt):
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## * add "dynamic_reconfigure" to
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## find_package(catkin REQUIRED COMPONENTS ...)
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## * uncomment the "generate_dynamic_reconfigure_options" section below
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## and list every .cfg file to be processed
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## Generate dynamic reconfigure parameters in the 'cfg' folder
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# generate_dynamic_reconfigure_options(
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# cfg/DynReconf1.cfg
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# cfg/DynReconf2.cfg
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# )
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###################################
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## catkin specific configuration ##
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###################################
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## The catkin_package macro generates cmake config files for your package
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## Declare things to be passed to dependent projects
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## INCLUDE_DIRS: uncomment this if your package contains header files
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## LIBRARIES: libraries you create in this project that dependent projects also need
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## CATKIN_DEPENDS: catkin_packages dependent projects also need
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## DEPENDS: system dependencies of this project that dependent projects also need
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catkin_package(
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# INCLUDE_DIRS include
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# LIBRARIES husky_mpc
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# CATKIN_DEPENDS geometry_msgs navigation_msgs roscpp rospy std_msgs
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# DEPENDS system_lib
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)
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###########
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## Build ##
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###########
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## Specify additional locations of header files
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## Your package locations should be listed before other locations
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include_directories(
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# include
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${catkin_INCLUDE_DIRS}
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)
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## Declare a C++ library
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# add_library(${PROJECT_NAME}
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# src/${PROJECT_NAME}/husky_mpc.cpp
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# )
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## Add cmake target dependencies of the library
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## as an example, code may need to be generated before libraries
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## either from message generation or dynamic reconfigure
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# add_dependencies(${PROJECT_NAME} ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})
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## Declare a C++ executable
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## With catkin_make all packages are built within a single CMake context
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## The recommended prefix ensures that target names across packages don't collide
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# add_executable(${PROJECT_NAME}_node src/husky_mpc_node.cpp)
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## Rename C++ executable without prefix
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## The above recommended prefix causes long target names, the following renames the
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## target back to the shorter version for ease of user use
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## e.g. "rosrun someones_pkg node" instead of "rosrun someones_pkg someones_pkg_node"
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# set_target_properties(${PROJECT_NAME}_node PROPERTIES OUTPUT_NAME node PREFIX "")
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## Add cmake target dependencies of the executable
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## same as for the library above
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# add_dependencies(${PROJECT_NAME}_node ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})
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## Specify libraries to link a library or executable target against
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# target_link_libraries(${PROJECT_NAME}_node
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# ${catkin_LIBRARIES}
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# )
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#############
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## Install ##
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#############
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# all install targets should use catkin DESTINATION variables
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# See http://ros.org/doc/api/catkin/html/adv_user_guide/variables.html
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## Mark executable scripts (Python etc.) for installation
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## in contrast to setup.py, you can choose the destination
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# install(PROGRAMS
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# scripts/my_python_script
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# DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}
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# )
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## Mark executables for installation
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## See http://docs.ros.org/melodic/api/catkin/html/howto/format1/building_executables.html
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# install(TARGETS ${PROJECT_NAME}_node
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# RUNTIME DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}
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# )
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## Mark libraries for installation
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## See http://docs.ros.org/melodic/api/catkin/html/howto/format1/building_libraries.html
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# install(TARGETS ${PROJECT_NAME}
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# ARCHIVE DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}
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# LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}
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# RUNTIME DESTINATION ${CATKIN_GLOBAL_BIN_DESTINATION}
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# )
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## Mark cpp header files for installation
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# install(DIRECTORY include/${PROJECT_NAME}/
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# DESTINATION ${CATKIN_PACKAGE_INCLUDE_DESTINATION}
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# FILES_MATCHING PATTERN "*.h"
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# PATTERN ".svn" EXCLUDE
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# )
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## Mark other files for installation (e.g. launch and bag files, etc.)
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# install(FILES
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# # myfile1
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# # myfile2
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# DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION}
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# )
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#############
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## Testing ##
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#############
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## Add gtest based cpp test target and link libraries
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# catkin_add_gtest(${PROJECT_NAME}-test test/test_husky_mpc.cpp)
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# if(TARGET ${PROJECT_NAME}-test)
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# target_link_libraries(${PROJECT_NAME}-test ${PROJECT_NAME})
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# endif()
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## Add folders to be run by python nosetests
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# catkin_add_nosetests(test)
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@ -1,74 +0,0 @@
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<?xml version="1.0"?>
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<package format="2">
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<name>husky_mpc</name>
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<version>0.0.0</version>
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<description>The husky_mpc package</description>
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<!-- One maintainer tag required, multiple allowed, one person per tag -->
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<!-- Example: -->
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<!-- <maintainer email="jane.doe@example.com">Jane Doe</maintainer> -->
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<maintainer email="marcello@todo.todo">marcello</maintainer>
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<!-- One license tag required, multiple allowed, one license per tag -->
|
||||
<!-- Commonly used license strings: -->
|
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<!-- BSD, MIT, Boost Software License, GPLv2, GPLv3, LGPLv2.1, LGPLv3 -->
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<license>TODO</license>
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<!-- Url tags are optional, but multiple are allowed, one per tag -->
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<!-- Optional attribute type can be: website, bugtracker, or repository -->
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<!-- Example: -->
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<!-- <url type="website">http://wiki.ros.org/husky_mpc</url> -->
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<!-- Author tags are optional, multiple are allowed, one per tag -->
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<!-- Authors do not have to be maintainers, but could be -->
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<!-- Example: -->
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<!-- <author email="jane.doe@example.com">Jane Doe</author> -->
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<!-- The *depend tags are used to specify dependencies -->
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<!-- Dependencies can be catkin packages or system dependencies -->
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<!-- Examples: -->
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<!-- Use depend as a shortcut for packages that are both build and exec dependencies -->
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<!-- <depend>roscpp</depend> -->
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<!-- Note that this is equivalent to the following: -->
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<!-- <build_depend>roscpp</build_depend> -->
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<!-- <exec_depend>roscpp</exec_depend> -->
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<!-- Use build_depend for packages you need at compile time: -->
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<!-- <build_depend>message_generation</build_depend> -->
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<!-- Use build_export_depend for packages you need in order to build against this package: -->
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<!-- <build_export_depend>message_generation</build_export_depend> -->
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<!-- Use buildtool_depend for build tool packages: -->
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<!-- <buildtool_depend>catkin</buildtool_depend> -->
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<!-- Use exec_depend for packages you need at runtime: -->
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<!-- <exec_depend>message_runtime</exec_depend> -->
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||||
<!-- Use test_depend for packages you need only for testing: -->
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<!-- <test_depend>gtest</test_depend> -->
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<!-- Use doc_depend for packages you need only for building documentation: -->
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<!-- <doc_depend>doxygen</doc_depend> -->
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<buildtool_depend>catkin</buildtool_depend>
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<build_depend>geometry_msgs</build_depend>
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<build_depend>nav_msgs</build_depend>
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<build_depend>roscpp</build_depend>
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<build_depend>rospy</build_depend>
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<build_depend>std_msgs</build_depend>
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<build_export_depend>geometry_msgs</build_export_depend>
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<build_export_depend>nav_msgs</build_export_depend>
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<build_export_depend>roscpp</build_export_depend>
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<build_export_depend>rospy</build_export_depend>
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<build_export_depend>std_msgs</build_export_depend>
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<exec_depend>geometry_msgs</exec_depend>
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<exec_depend>nav_msgs</exec_depend>
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<exec_depend>roscpp</exec_depend>
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<exec_depend>rospy</exec_depend>
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<exec_depend>std_msgs</exec_depend>
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<!-- The export tag contains other, unspecified, tags -->
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<export>
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<!-- Other tools can request additional information be placed here -->
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</export>
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</package>
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#! /usr/bin/env python
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import rospy
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import numpy as np
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from nav_msgs.msg import Odometry
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from geometry_msgs.msg import Twist
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from utils import compute_path_from_wp
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from cvxpy_mpc import optimize
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# classes
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class Node():
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def __init__(self):
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rospy.init_node('mpc_node')
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N = 5 #number of state variables
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M = 2 #number of control variables
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T = 20 #Prediction Horizon
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dt = 0.25 #discretization step
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# State for the robot mathematical model
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self.state = None
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# starting guess output
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self.opt_u = np.zeros((M,T))
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self.opt_u[0,:] = 1 #m/s
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self.opt_u[1,:] = np.radians(0) #rad/s
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# Interpolated Path to follow given waypoints
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self.path = compute_path_from_wp([0,20,30,30],[0,0,10,20])
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self._cmd_pub = rospy.Publisher(rospy.get_namespace() + 'husky_velocity_controller/cmd_vel', Twist, queue_size=10)
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self._odom_sub = rospy.Subscriber(rospy.get_namespace() +'husky_velocity_controller/odom', Odometry, self._odom_cb, queue_size=1)
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def run(self):
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while 1:
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if self.state is not None:
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#optimization loop
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self.opt_u = optimize(self.state,
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self.opt_u,
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self.path)
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msg = Twist()
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msg.linear.x=self.opt_u[0,1]
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msg.angular.z=self.opt_u[0,1]
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self._cmd_pub(msg)
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def _odom_cb(self,odom):
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'''
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Updates state with latest odometry.
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:param odom: nav_msgs.msg.Odometry
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'''
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state = np.zeros(3)
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# Update current position
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state[0] = odom.pose.pose.position.x
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state[1] = odom.pose.pose.position.y
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# Update current orientation
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_, _, state[2] = euler_from_quaternion(
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[odom.pose.pose.orientation.x,
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odom.pose.pose.orientation.y,
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odom.pose.pose.orientation.z,
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odom.pose.pose.orientation.w])
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self.state = state
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def main():
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ros_node=Node()
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try:
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ros_node.run()
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except rospy.exceptions.ROSException as e:
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sys.exit(e)
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if __name__ == '__main__':
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main()
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theta = x_bar[2]
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psi = x_bar[3]
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cte = x_bar[4]
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v = u_bar[0]
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w = u_bar[1]
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A = np.zeros((N,N))
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A[0,2]=-v*np.sin(theta)
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A[1,2]=v*np.cos(theta)
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A[4,3]=v*np.cos(-psi)
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A_lin=np.eye(N)+dt*A
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B = np.zeros((N,M))
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B[0,0]=np.cos(theta)
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B[1,0]=np.sin(theta)
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@ -36,10 +36,10 @@ def get_linear_model(x_bar,u_bar):
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B[3,1]=-1
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B[4,0]=np.sin(-psi)
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B_lin=dt*B
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f_xu=np.array([v*np.cos(theta),v*np.sin(theta),w,-w,v*np.sin(-psi)]).reshape(N,1)
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C_lin = dt*(f_xu - np.dot(A,x_bar.reshape(N,1)) - np.dot(B,u_bar.reshape(M,1)))
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return A_lin,B_lin,C_lin
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def calc_err(state,path):
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@ -58,7 +58,7 @@ def calc_err(state,path):
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try:
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v = [path[0,nn_idx+1] - path[0,nn_idx],
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path[1,nn_idx+1] - path[1,nn_idx]]
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path[1,nn_idx+1] - path[1,nn_idx]]
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v /= np.linalg.norm(v)
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d = [path[0,nn_idx] - state[0],
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@ -74,17 +74,17 @@ def calc_err(state,path):
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path_ref_vect = [np.cos(path[2,target_idx] + np.pi / 2),
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np.sin(path[2,target_idx] + np.pi / 2)]
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#heading error w.r.t path frame
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psi = path[2,target_idx] - state[2]
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# the cross-track error is given by the scalar projection of the car->wp vector onto the faxle versor
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#cte = np.dot([dx[target_idx], dy[target_idx]],front_axle_vect)
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cte = np.dot([dx[target_idx], dy[target_idx]],path_ref_vect)
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return target_idx,psi,cte
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def optimize(starting_state,u_bar,track);
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def optimize(starting_state,u_bar,track):
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'''
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:param starting_state:
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:param u_bar:
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@ -100,7 +100,7 @@ def optimize(starting_state,u_bar,track);
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M = 2 #number of control variables
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T = 20 #Prediction Horizon
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dt = 0.25 #discretization step
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#Starting Condition
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x0 = np.zeros(N)
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x0[0] = starting_state[0]
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@ -141,7 +141,7 @@ def optimize(starting_state,u_bar,track);
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idx,_,_ = calc_err(x_bar[:,t],track)
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delta_x = track[:,idx]-x[0:3,t]
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cost+= cp.quad_form(delta_x,10*np.eye(3))
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# Tracking last time step
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if t == T:
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idx,_,_ = calc_err(x_bar[:,t],track)
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# Actuation rate of change
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if t < (T - 1):
|
||||
cost += cp.quad_form(u[:, t + 1] - u[:, t], 25*np.eye(M))
|
||||
|
||||
|
||||
# Actuation effort
|
||||
cost += cp.quad_form( u[:, t],1*np.eye(M))
|
||||
|
||||
|
||||
# Constrains
|
||||
A,B,C=get_linear_model(x_bar[:,t],u_bar[:,t])
|
||||
constr += [x[:,t+1] == A*x[:,t] + B*u[:,t] + C.flatten()]
|
||||
|
||||
# sums problem objectives and concatenates constraints.
|
||||
constr += [x[:,0] == x_sim[:,sim_time]] # starting condition
|
||||
constr += [x[:,0] == x0] # starting condition
|
||||
constr += [u[0, :] <= MAX_SPEED]
|
||||
constr += [u[0, :] >= MIN_SPEED]
|
||||
constr += [cp.abs(u[1, :]) <= MAX_STEER_SPEED]
|
||||
|
||||
|
||||
# Solve
|
||||
prob = cp.Problem(cp.Minimize(cost), constr)
|
||||
solution = prob.solve(solver=cp.ECOS, verbose=False)
|
||||
|
||||
|
||||
#retrieved optimized U and assign to u_bar to linearize in next step
|
||||
u_bar=np.vstack((np.array(u.value[0, :]).flatten(),
|
||||
(np.array(u.value[1, :]).flatten())))
|
||||
|
||||
return u_bar
|
||||
|
||||
return u_bar
|
||||
|
|
@ -0,0 +1,91 @@
|
|||
#! /usr/bin/env python
|
||||
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib import animation
|
||||
|
||||
from utils import compute_path_from_wp
|
||||
from cvxpy_mpc import optimize
|
||||
|
||||
import sys
|
||||
import time
|
||||
|
||||
# classes
|
||||
class MPC():
|
||||
|
||||
def __init__(self):
|
||||
|
||||
# State for the robot mathematical model [x,y,heading]
|
||||
self.state = np.zeros(3)
|
||||
# Sim step
|
||||
self.dt = 0.25
|
||||
|
||||
# starting guess output
|
||||
N = 5 #number of state variables
|
||||
M = 2 #number of control variables
|
||||
T = 20 #Prediction Horizon
|
||||
self.opt_u = np.zeros((M,T))
|
||||
self.opt_u[0,:] = 1 #m/s
|
||||
self.opt_u[1,:] = np.radians(0) #rad/s
|
||||
|
||||
# Interpolated Path to follow given waypoints
|
||||
self.path = compute_path_from_wp([0,20,30,30],[0,0,10,20])
|
||||
|
||||
#Initialise plot
|
||||
# First set up the figure, the axis, and the plot element we want to animate
|
||||
plt.style.use("ggplot")
|
||||
self.fig = plt.figure()
|
||||
plt.ion()
|
||||
plt.show()
|
||||
|
||||
def run(self):
|
||||
'''
|
||||
'''
|
||||
|
||||
while 1:
|
||||
if self.state is not None:
|
||||
|
||||
#optimization loop
|
||||
start=time.time()
|
||||
self.opt_u = optimize(self.state,
|
||||
self.opt_u,
|
||||
self.path)
|
||||
|
||||
print("CVXPY Optimization Time: {:.4f}s".format(time.time()-start))
|
||||
|
||||
self.update_sim(self.opt_u[0,1],self.opt_u[1,1])
|
||||
|
||||
self.plot_sim()
|
||||
|
||||
def update_sim(self,lin_v,ang_v):
|
||||
'''
|
||||
Updates state.
|
||||
|
||||
:param lin_v: float
|
||||
:param ang_v: float
|
||||
'''
|
||||
|
||||
self.state[0] = self.state[0] +lin_v*np.cos(self.state[2])*self.dt
|
||||
self.state[1] = self.state[1] +lin_v*np.sin(self.state[2])*self.dt
|
||||
self.state[2] = self.state[2] +ang_v*self.dt
|
||||
|
||||
def plot_sim(self):
|
||||
plt.clf()
|
||||
self.ax = plt.axes(xlim=(np.min(self.path[0,:])-1, np.max(self.path[0,:])+1),
|
||||
ylim=(np.min(self.path[1,:])-1, np.max(self.path[1,:])+1))
|
||||
|
||||
self.track, = self.ax.plot(self.path[0,:],self.path[1,:], "g-", label="reference track")
|
||||
self.vehicle, = self.ax.plot([self.state[0]], [self.state[1]], "r*", label="vehicle path")
|
||||
plt.legend()
|
||||
plt.draw()
|
||||
plt.pause(0.1)
|
||||
|
||||
def do_sim():
|
||||
sim=MPC()
|
||||
try:
|
||||
sim.run()
|
||||
except Exception as e:
|
||||
sys.exit(e)
|
||||
|
||||
if __name__ == '__main__':
|
||||
do_sim()
|
||||
|
|
@ -1,12 +1,10 @@
|
|||
import numpy as np
|
||||
from scipy.integrate import odeint
|
||||
from scipy.interpolate import interp1d
|
||||
import cvxpy as cp
|
||||
|
||||
def compute_path_from_wp(start_xp, start_yp, step = 0.1):
|
||||
"""
|
||||
Interpolation range is computed to assure one point every fixed distance step [m].
|
||||
|
||||
|
||||
:param start_xp: array_like, list of starting x coordinates
|
||||
:param start_yp: array_like, list of starting y coordinates
|
||||
:param step: float, interpolation distance [m] between consecutive waypoints
|
||||
|
|
@ -21,13 +19,15 @@ def compute_path_from_wp(start_xp, start_yp, step = 0.1):
|
|||
section_len = np.sum(np.sqrt(np.power(np.diff(start_xp[idx:idx+2]),2)+np.power(np.diff(start_yp[idx:idx+2]),2)))
|
||||
|
||||
interp_range = np.linspace(0,1,section_len/delta)
|
||||
|
||||
|
||||
fx=interp1d(np.linspace(0,1,2),start_xp[idx:idx+2],kind=1)
|
||||
fy=interp1d(np.linspace(0,1,2),start_yp[idx:idx+2],kind=1)
|
||||
|
||||
|
||||
final_xp=np.append(final_xp,fx(interp_range))
|
||||
final_yp=np.append(final_yp,fy(interp_range))
|
||||
|
||||
|
||||
dx = np.append(0, np.diff(final_xp))
|
||||
dy = np.append(0, np.diff(final_yp))
|
||||
theta = np.arctan2(dy, dx)
|
||||
|
||||
return np.vstack((final_xp,final_yp,theta))
|
||||
|
|
@ -1158,13 +1158,6 @@
|
|||
"plt.tight_layout()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
|
|
|||
|
|
@ -1158,13 +1158,6 @@
|
|||
"plt.tight_layout()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
|
|
|||
Loading…
Reference in New Issue