The 72 h following a natural or man-made disaster are essential in search and rescue operations. Post-disaster environments usually possess damage to infrastructure that delay rescue-operations. Motion planning systems used in such missions need to use algorithms that generate the optimal paths for unmanned aerial vehicles (UAVs) and other autonomous robots in the least possible amount of time. We have compared various algorithms that efficiently deal with environments with narrow passages or densely packed obstacles, similar to real-world environments after natural disasters. We have analyzed three algorithms: Hybrid RRT*, Informed Quick RRT*, and Informed Quick RRT*-Connect, and found that the best results are provided by Informed Quick RRT*-Connect. The proposed algorithm not only generates better initial solutions but also converges to the near-optimal solution in lesser amount of time as compared to the other two algorithms in analogous obstacle scenarios. This makes it ideal for use in motion planning systems that deal with the navigation of autonomous robots in post-disaster environments.