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Robot Planning and its Application Course - Student Interface

Code Documentation

The documentation of the code can be found HERE.

The paper containing the details about the project and the design choices, instead, can be found HERE.

Usage

  1. Build the simulator using catkin build, then source ~/workspace/simulator/environment.sh and source ~/workspace/project/environment.sh
  2. Build the ApplicationRoboticsStudentinterface with cd build, cmake .. and then make, then source ~/workspace/simulator/environment.sh and source ~/workspace/project/environment.sh
  3. Go to cd $AR_config_dir, gedit default_implementation.config and change the "planning" variable to false (This passage needs to be done only once)
  4. In four different terminals, go under the directory workspace and run the following commands, one for each terminal:
  • AR_simulator n:=2 (n is the number of robots to spawn)
  • AR_pipeline n:=2
  • AR_rviz n:=2
  • AR_plan

[NOTE: ALWAY remember to source the environmet before AR commands, meaning that you need to run source ~/workspace/simulator/environment.sh and source ~/workspace/project/environment.sh] 5. Run AR_plan and the planned path both for the evader and the pursuer will be visible. 6. Run AR_run to move the robots accordingly to the path that was described in the plan phase.

Structure of our solution

The main elements implemented in order to fulfill the project requirements are:

  • Clipper library: used for polygon offsetting and join of obstacles.
  • Visibility Graph generation: generation of the shortest path roadmap. The implemented algorithm follows the plane-sweep principle presented in Section 6.2.2 of the Motion Planning book provided to us by the professor. In particular, the chapter 15 of Computational Geometry - Algorithms and Applications has been followed for the details of the visibility graph generation implementation. In order to optimize the algorithm, a special datastructure called OpenEdges has been implemented, following the open-source project pyvisgraph, written in Python. Final complexity: O(n^2 logn), where n is the number of vertices.
  • Dijkstra Shortest Path: it was implemented using C++ STL sets, which are self-balancing binary search trees. Final complexity: O(E logV), where E is the number of edges and V is the number of vertices of the graph.
  • Multipoint Markov-Dubins Problem: solved with an Iterative Dynamic Programming approach, explained during class. Final complexity: O(n k^2), where n is the number of points and k is the number of discretised angles.
  • Collision detection: solved with a basic algorithm to check Arc-Segment intersections, that must be run at each iteration of the Dubins shortest path calculation.
  • Pursuer-Evader game: a simulation of a pursuer-evader game, in which one robot - the evader - tries to escape to a destination, while another robot - the pursuer - has to intercept it. The details on the developed algorithm can be found on the report linked above.