IRIS (Incremental Random Inspection-roadmap Search)

Linux / macOS

This is code for paper Toward Asymptotically-Optimal Inspection Planning via Efficient Near-Optimal Graph Search.

Requirements

• GCC(for Linux) - v7.4
• Boost - v1.65.0
• CMake - v3.8
• Eigen3 - v3.3.4
• OMPL - the Open Motion Planning Library v1.3.2

Installation

1. First clone code to your local repository:

   git clone git@github.com:UNC-Robotics/IRIS.git [{path to your local repository}]
   git submodule update --init --recursive
   

2. Download data from google drive to your local respository, uncompress.

3. Install all dependencies. If you install dependencies to your specified directories, you will need to provide the information when compiling.

4. Compile:

   cd {path to your local repository}
   mkdir build
   cd build
   cmake ..
   make
   

5. Tested environment:

   • Ubuntu 18.04 (gcc 7.4.0)
   • Ubuntu 16.04 (gcc 7.4.0)
   • Ubuntu 14.04 (gcc 7.2.0)
   • macOS Mojave Version 10.14.6 (clang-1001.0.46.4)
   • macOS Catalina Version 10.15 (clang-1100.0.33.8)

Usage

1. Specify which scenario to use in include/global_common.h:

   #define USE_CRISP 0
   #define USE_PLANAR 0
   

   If USE_CRISP is set to 1, then the CRISP robot is used.

   If USE_CRISP is set to 0 and USE_PLANAR is set to 1, then the planar robot is used.

   If both USE_CRISP and USE_PLANAR are set to 0, then the drone robot is used.

   Important for macbook users:

   OMPL uses C++17 deprecated functions, and clang reports errors when you set C++ standard to 17. A simple way to solve this is to disable drone robot (which is done by the current cmake files), all you need to do is avoid setting both USE_CRISP and USE_PLANAR to 0. If you still want to use the drone robot, please replace {OMPL_Source}/src/ompl/datastructures/NearestNeighborsGNAT.h with the one in external folder, rebuild and reinstall OMPL, finally change line 23 in root CMakeLists.txt to set(USE_C++17 1).

2. Build a graph:

   cd {path to your local repository}/build
   ./app/build_graph seed num_vertex file_to_write 
   

3. Search a graph:

   cd {path to your local repository}/build
   ./app/search_graph file_to_read initial_p initial_eps tightening_rate method file_to_write
   

   Here, three different search methods are provided:

   0 -- no lazy computation

   1 -- lazy A*

   2 -- complete lazy

Robot Background

This repository implements an inspection planning algorithm and demonstrates its functionality on three different robots.

1. The CRISP robot

   The first one is a continuum, reconfigurable, parallel robot designed for incisionless surgery (i.e., CRISP robot). This robot concept combines the flexibility and minimally invasive access of continuum robots with the stiffness of parallel robots and the ability of reconfigurable robots to adjust for changing task requirements. CRISP robots consist of multiple elastic members (typically flexible needles made of materials such as superelastic Nitinol) that are connected inside a body cavity using wire snare loops. The needles form a flexible parallel structure that is controlled from outside the body with robot manipulators. Previous research on CRISP robots includes mechanics-based modeling [1], shape sensing and estimation [2], motion planning [3], and design optimization [4].

   [1] A. W. Mahoney, P. L. Anderson, P. J. Swaney, F. Maldonado, and R. J. Webster, "Reconfigurable Parallel Continuum Robots for Incisionless Surgery," 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, 2016, pp. 4330-4336. doi: 10.1109/IROS.2016.7759637

   [2] P. L. Anderson, A. W. Mahoney, and R. J. Webster, "Continuum Reconfigurable Parallel Robots for Surgery: Shape Sensing and State Estimation With Uncertainty," in IEEE Robotics and Automation Letters, vol. 2, no. 3, pp. 1617-1624, July 2017. doi: 10.1109/LRA.2017.2678606

   [3] A. Kuntz, A. W. Mahoney, N. E. Peckman, P. L. Anderson, F. Maldonado, R. J. Webster, and R. Alterovitz, "Motion Planning for Continuum Reconfigurable Incisionless Surgical Parallel Robots," 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, 2017, pp. 6463-6469. doi: 10.1109/IROS.2017.8206553

   [4] A. Kuntz, C. Bowen, C. Baykal, A. W. Mahoney, P. L. Anderson, F. Maldonado, R. J. Webster, and R. Alterovitz, "Kinematic Design Optimization of a Parallel Surgical Robot to Maximize Anatomical Visibility via Motion Planning," 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, 2018, pp. 926-933. doi: 10.1109/ICRA.2018.8461135

   

2. The planar-link-camera robot

   The second one is a multi-link planar manipulator with a camera as end effector.

   

3. The quadroter

   The third one is a unmanned aerial vehicle (UAV), a quadroter more specifically. It tries to inspect a bridge structure provided as a mesh.