Autonomous drones play a vital role in Disaster mitigation systems and commercial good delivery systems. The problem involves finding the shortest path between the delivery points while simultaneously avoiding stationary obstacles (for example high raised buildings) and moving obstacles like other drones. The path needs to be continuously changed based on the telemetry from other drones or based on the addition of new way-points. This is major issue in planning problems. Any algorithm will have to make complex choices like abandoning shortest paths to avoid collisions. In this paper we propose a tangent algorithm which chooses paths based on many performance measures like number of obstacles in current path and the future path and the distance to the next obstacle. The path has very few sharp turns and the locations of these turns are calculated during the path planning. The performance evaluation on different environments demonstrates that the algorithm will be particularly faster in case of both sparse and dense obstacles. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.