Minor folder structure arranging
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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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## 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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<?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 -->
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<!-- 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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import numpy as np
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from scipy.integrate import odeint
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from scipy.interpolate import interp1d
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import cvxpy as cp
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def get_linear_model(x_bar,u_bar):
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"""
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"""
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# Control problem statement.
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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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x = x_bar[0]
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y = x_bar[1]
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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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B[2,1]=1
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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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"""
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Finds psi and cte w.r.t. the closest waypoint.
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:param state: array_like, state of the vehicle [x_pos, y_pos, theta]
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:param path: array_like, reference path ((x1, x2, ...), (y1, y2, ...), (th1 ,th2, ...)]
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:returns: (float,float)
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"""
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dx = state[0]-path[0,:]
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dy = state[1]-path[1,:]
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dist = np.sqrt(dx**2 + dy**2)
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nn_idx = np.argmin(dist)
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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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v /= np.linalg.norm(v)
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d = [path[0,nn_idx] - state[0],
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path[1,nn_idx] - state[1]]
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if np.dot(d,v) > 0:
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target_idx = nn_idx
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else:
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target_idx = nn_idx+1
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except IndexError as e:
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target_idx = nn_idx
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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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'''
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:param starting_state:
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:param u_bar:
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:param track:
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:returns:
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'''
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MAX_SPEED = 1.25
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MIN_SPEED = 0.75
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MAX_STEER_SPEED = 1.57/2
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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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#Starting Condition
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x0 = np.zeros(N)
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x0[0] = starting_state[0]
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x0[1] = starting_state[1]
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x0[2] = starting_state[2]
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_,psi,cte = calc_err(x0,track)
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x0[3]=psi
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x0[4]=cte
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# Prediction
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x_bar=np.zeros((N,T+1))
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x_bar[:,0]=x0
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for t in range (1,T+1):
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xt=x_bar[:,t-1].reshape(5,1)
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ut=u_bar[:,t-1].reshape(2,1)
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A,B,C=get_linear_model(xt,ut)
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xt_plus_one = np.squeeze(np.dot(A,xt)+np.dot(B,ut)+C)
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_,psi,cte = calc_err(xt_plus_one,track)
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xt_plus_one[3]=psi
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xt_plus_one[4]=cte
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x_bar[:,t]= xt_plus_one
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#CVXPY Linear MPC problem statement
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cost = 0
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constr = []
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x = cp.Variable((N, T+1))
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u = cp.Variable((M, T))
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for t in range(T):
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# Tracking
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if t > 0:
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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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delta_x = track[:,idx]-x[0:3,t]
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cost+= cp.quad_form(delta_x,100*np.eye(3))
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# Actuation rate of change
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if t < (T - 1):
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cost += cp.quad_form(u[:, t + 1] - u[:, t], 25*np.eye(M))
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# Actuation effort
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cost += cp.quad_form( u[:, t],1*np.eye(M))
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# Constrains
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A,B,C=get_linear_model(x_bar[:,t],u_bar[:,t])
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constr += [x[:,t+1] == A*x[:,t] + B*u[:,t] + C.flatten()]
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# sums problem objectives and concatenates constraints.
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constr += [x[:,0] == x_sim[:,sim_time]] # starting condition
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constr += [u[0, :] <= MAX_SPEED]
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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
|
|
@ -0,0 +1,84 @@
|
|||
#! /usr/bin/env python
|
||||
|
||||
import rospy
|
||||
import numpy as np
|
||||
|
||||
from nav_msgs.msg import Odometry
|
||||
|
||||
from geometry_msgs.msg import Twist
|
||||
|
||||
from utils import compute_path_from_wp
|
||||
from cvxpy_mpc import optimize
|
||||
|
||||
# classes
|
||||
class Node():
|
||||
|
||||
def __init__(self):
|
||||
|
||||
rospy.init_node('mpc_node')
|
||||
|
||||
N = 5 #number of state variables
|
||||
M = 2 #number of control variables
|
||||
T = 20 #Prediction Horizon
|
||||
dt = 0.25 #discretization step
|
||||
|
||||
# State for the robot mathematical model
|
||||
self.state = None
|
||||
|
||||
# starting guess output
|
||||
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])
|
||||
|
||||
self._cmd_pub = rospy.Publisher(rospy.get_namespace() + 'husky_velocity_controller/cmd_vel', Twist, queue_size=10)
|
||||
self._odom_sub = rospy.Subscriber(rospy.get_namespace() +'husky_velocity_controller/odom', Odometry, self._odom_cb, queue_size=1)
|
||||
|
||||
def run(self):
|
||||
while 1:
|
||||
if self.state is not None:
|
||||
|
||||
#optimization loop
|
||||
self.opt_u = optimize(self.state,
|
||||
self.opt_u,
|
||||
self.path)
|
||||
|
||||
msg = Twist()
|
||||
msg.linear.x=self.opt_u[0,1]
|
||||
msg.angular.z=self.opt_u[0,1]
|
||||
|
||||
self._cmd_pub(msg)
|
||||
|
||||
def _odom_cb(self,odom):
|
||||
'''
|
||||
Updates state with latest odometry.
|
||||
|
||||
:param odom: nav_msgs.msg.Odometry
|
||||
'''
|
||||
|
||||
state = np.zeros(3)
|
||||
|
||||
# Update current position
|
||||
state[0] = odom.pose.pose.position.x
|
||||
state[1] = odom.pose.pose.position.y
|
||||
|
||||
# Update current orientation
|
||||
_, _, state[2] = euler_from_quaternion(
|
||||
[odom.pose.pose.orientation.x,
|
||||
odom.pose.pose.orientation.y,
|
||||
odom.pose.pose.orientation.z,
|
||||
odom.pose.pose.orientation.w])
|
||||
|
||||
self.state = state
|
||||
|
||||
def main():
|
||||
ros_node=Node()
|
||||
try:
|
||||
ros_node.run()
|
||||
except rospy.exceptions.ROSException as e:
|
||||
sys.exit(e)
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
|
@ -0,0 +1,33 @@
|
|||
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
|
||||
:returns: array_like, of shape (3,N)
|
||||
"""
|
||||
|
||||
final_xp=[]
|
||||
final_yp=[]
|
||||
delta = step #[m]
|
||||
|
||||
for idx in range(len(start_xp)-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)
|
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Reference in New Issue