The essential factor in developing multi-robot systems is the generation of an optimal path for task completion by multiple robots. This paper studies the recent publications and provides a detailed review of the path planning approaches to avoid collisions in uncertain environments. In this article, path planning approaches for multiple robots are categorized primarily into classical, heuristic, and artificial intelligence-based methods. Among the heuristic approaches, bio-inspired approaches are mostly employed to optimize the classical approaches to enhance their adaptability. The articles are analyzed based on static and dynamic scenarios, real-time experiments, and simulations involving hybrid solutions. The increasing focus on using hybrid approaches in dynamic environments is found mostly in the papers employing heuristic and AI-based approaches. In real-time applications, AI-based approaches are highly implemented in comparison to heuristic and classical approaches. The findings from this review can help researchers select the appropriate approach to overcome the limitations in designing efficient multi-robot systems.