Update README.md

Author: Fotis Dimeas

README.md

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 This repository includes the Matlab code that was used in the ICRA 2016 paper and the extended method in C++ that is presented in the RCIM journal. 
 
 The C++ implementation includes performance constraints in both translational and rotational axes. It also includes 3 ways of calculating them:
-⋅⋅* Serial: Each direction is calculated after the other. It might take a while
-⋅⋅* Parallel: Each direction is calculated in its own thread worker. Function returns when all calculations are over. This usually works in less than 1ms. Better have a multi-thread CPU.
-⋅⋅* Parallel non-blocking: Same as above but the function returns immediately. This can work in slow computers. WARNING! Be very careful when you use it, synchronization issues of the constraint forces may arise.
+* Serial: Each direction is calculated after the other. It might take a while
+* Parallel: Each direction is calculated in its own thread worker. Function returns when all calculations are over. This usually works in less than 1ms. Better have a multi-thread CPU.
+* Parallel non-blocking: Same as above but the function returns immediately. This can work in slow computers. WARNING! Be very careful when you use it, synchronization issues of the constraint forces may arise.
 
 # Usage:
 ## C++
-> g++ demo.cpp performanceConstraints.cpp Jacobian.cpp  -o demo -larmadillo -lpthread -std=c++11
+> g++ demo.cpp performanceConstraints.cpp Jacobian.cpp  -o demo -larmadillo -pthread -std=c++11
 
 The Jacobian matrix that is provided in symbolic form is for the KUKA LWR 4+