Path planning is the way of determination of a collision free path between start and goal position through obstacles cluttered in a workspace. Though it is a complex problem, but it is an essential task for the navigation and controlling the motion of autonomous robot manipulators. This NP-complete problem (those problems is difficult to solve specially in a dynamic environment where the optimal path needs to be re-routed in real time when a new obstacle appears. This paper provides two categories of path planning approaches:-Deterministic and Probabilistic approaches. Deterministic methods allow achieving the same result in each execution with the same initial conditions. They are perfectly predictable, hence suitable for static environment, but not effective when they are used in a real time environment as there could be sudden changes in environment. The most used solution to overcome the problem of real time environment are the probabilistic methods such as Particle swarm optimization[pso], Ant colony optimization[aco], genetic algorithm[ga], multi agent path planning,etc.

Deterministic algorithm probabilistic algorithm aco pso ga A*algorithm dijkstra multi agent path planning.