Updates README and changes name of python binding library

Author: haiot4105

CMakeLists.txt

@@ -71,6 +71,9 @@ pybind11_add_module(mppi_ca_cpp python_bind/converters.hpp python_bind/converter
 target_link_libraries(mppi_ca_cpp PRIVATE xtensor-python Python::NumPy)
 target_link_libraries(mppi_ca_cpp PRIVATE controller_library ortools::ortools)
 
+set_target_properties(mppi_ca_cpp PROPERTIES PREFIX "")
+set_target_properties(mppi_ca_cpp PROPERTIES OUTPUT_NAME "mppi_ca_cpp")
+set_target_properties(mppi_ca_cpp PROPERTIES SUFFIX ".so")
 
 add_custom_target(mppi_all)
 add_dependencies(mppi_all

README.md

@@ -6,7 +6,7 @@
 </p>
 
 
-Implementation of distributed multi-agent collision avoidance algorithm based on the seminal sampling-based model predictive control algorithm, i.e., _MPPI_ [[1]()], that originally solves a single-agent problem. We enhance it by introducing safe distributions for the multi-agent setting that are derived from the _Optimal Reciprocal Collision Avoidance (ORCA)_ linear constraints [[2]()], an established approach from the multi-agent navigation domain. 
+Implementation of distributed multi-agent collision avoidance algorithm based on the seminal sampling-based model predictive control algorithm, i.e., _MPPI_ [[1](https://homes.cs.washington.edu/~bboots/files/InformationTheoreticMPC.pdf)], that originally solves a single-agent problem. We enhance it by introducing safe distributions for the multi-agent setting that are derived from the _Optimal Reciprocal Collision Avoidance (ORCA)_ linear constraints [[2](https://gamma.cs.unc.edu/ORCA/publications/ORCA.pdf)], an established approach from the multi-agent navigation domain. 
 
 
 This is supplementary code for paper "Model Predictive Path Integral for Decentralized Multi-Agent Collision Avoidance" which is under consideration in the PeerJ Computer Science journal. 
@@ -28,7 +28,7 @@ To build and run the project you can use CMake, CMakeLists.txt file is available
 
 ### Prerequisites
 
-- CMake 3.20
+- CMake (3.20) and make (4.4.1)
 - C++ compiler with C++17 standart support (e.g. GCC 13.2)
 - Python 3.11 with next libraries:
     - numpy (1.26.3)
@@ -57,12 +57,44 @@ Download current repository to your local machine. Use:
 
 ### Build
 
-TODO
+Go to the directory with the project and create a subdirectory `build/release`. Go to the new subdirectory.
 
+```bash
+mkdir -p build/release
+cd build/release
+```
+
+Use the CMake and make tools to build the project. You can use the `-j` flag to improve building peroformance, but this may cause errors in case of resource deficit (in this case, it is worth specifying the number of processor cores to build, e.g. `-j4`)
+
+```bash
+cmake ../../ -DCMAKE_BUILD_TYPE=Release 
+make
+```
+
+or 
+
+```bash
+cmake ../../ -DCMAKE_BUILD_TYPE=Release 
+make -j
+```
 ### Launch an Example
 
-TODO
+Go to the example directory and run jupyter noterbook. After that choose `example.ipynb` file and run it.
+
+```bash
+cd ../../example
+jupyter notebook
+```
+
+The notebook contains a circular scenario (see figure below), the size of the circle and the number of agents can be changed by updating variables `CIRC_R` and `AGENTS_NUM`.
+
+```python
+CIRC_R = 6 # size of the circle
+AGENTS_NUM = 5 # number of agents
+```
+
 
 ## References
 
-TODO
+1. [Williams, G., Wagener, N., Goldfain, B., Drews, P., Rehg, J. M., Boots, B., and Theodorou, E. A. (2017b). Information theoretic MPC for model-based reinforcement learning. In 2017 IEEE International Conference on Robotics and Automation (ICRA), pages 1714–1721](https://homes.cs.washington.edu/~bboots/files/InformationTheoreticMPC.pdf)
+2. [Van Den Berg, J., Guy, S. J., Lin, M., and Manocha, D. (2011). Reciprocal n-body collision avoidance. In Robotics research, pages 3–19.](https://gamma.cs.unc.edu/ORCA/publications/ORCA.pdf)

example/example.ipynb

@@ -7,7 +7,7 @@
 from params_names import *
 from os.path import dirname
 
-sys.path.append(dirname("../build/release/mppi_ca_cpp.cpython-311-x86_64-linux-gnu.so"))
+sys.path.append(dirname("../build/release/mppi_ca_cpp.so"))
 import mppi_ca_cpp