This paper presents a randomized path planning algorithm for articulated robots with closed kinematic chains. The algorithm extends traditional randomized path planning techniques, originally developed for open-chain systems, to handle the complexities of closed-chain systems. The key challenge in path planning for closed kinematic chains is the presence of closure constraints, which require the robot's links to maintain specific geometric relationships. The proposed algorithm addresses this by implementing key primitive operations such as generating random free configurations and generating local paths that satisfy the closure constraints. The algorithm is demonstrated on a variety of problems, including manipulation planning with multiple open-chain manipulators that cooperatively grasp an object and planning for reconfigurable robots where links may be arranged in a loop to ease manipulation or locomotion. The algorithm is also applicable beyond robotics, including computer graphics, computational chemistry, and virtual prototyping, where high degrees of freedom and closure constraints are common. The paper shows that the proposed algorithm can be applied to high-dimensional problems and presents computed results for such cases. The algorithm is implemented using a randomized approach that generates random configurations and paths, ensuring that the closure constraints are satisfied. The algorithm is shown to be effective in navigating through environments with obstacles and maintaining the required geometric relationships between the robot's links. The results demonstrate the feasibility of the algorithm for general closed kinematic chains and highlight its potential for a wide range of applications.This paper presents a randomized path planning algorithm for articulated robots with closed kinematic chains. The algorithm extends traditional randomized path planning techniques, originally developed for open-chain systems, to handle the complexities of closed-chain systems. The key challenge in path planning for closed kinematic chains is the presence of closure constraints, which require the robot's links to maintain specific geometric relationships. The proposed algorithm addresses this by implementing key primitive operations such as generating random free configurations and generating local paths that satisfy the closure constraints. The algorithm is demonstrated on a variety of problems, including manipulation planning with multiple open-chain manipulators that cooperatively grasp an object and planning for reconfigurable robots where links may be arranged in a loop to ease manipulation or locomotion. The algorithm is also applicable beyond robotics, including computer graphics, computational chemistry, and virtual prototyping, where high degrees of freedom and closure constraints are common. The paper shows that the proposed algorithm can be applied to high-dimensional problems and presents computed results for such cases. The algorithm is implemented using a randomized approach that generates random configurations and paths, ensuring that the closure constraints are satisfied. The algorithm is shown to be effective in navigating through environments with obstacles and maintaining the required geometric relationships between the robot's links. The results demonstrate the feasibility of the algorithm for general closed kinematic chains and highlight its potential for a wide range of applications.