Artificial Potential Fields (APFs) are a local motion planning technique widely used in the field of robotics. The use of this reactive technique has surged due to the production of advanced robots, which are ever increasingly being deployed in dynamic and unknown environments. However, the success of this approach is limited due to inherent local minima issues. While improved APF-based methods have been developed for the motion planning of mobile robots, for more complex robots such as robotic manipulators, the existing APF approaches are still limited. Thus, the specific aim of this thesis is to develop improved APF motion planning techniques for manipulators. Firstly, the common types of local minima specific to manipulator applications are defined. These are then addressed by combining APF functions with novel motion planning techniques, including a goal configuration sampling algorithm and a subgoal selection algorithm based on expanded convex hulls. These algorithms are used to identify the final configurations which solve the motion planning problem and subsequently plot a collision-free path, around any locally detected obstacles, to reach one of the valid goal configurations. This intelligent motion planning overcomes the naivety of the APF approach, assisting it to avoid the inherent local minima problems. This results in an APF-based motion planner which significantly improves on the existing APF( approach for manipulators. Additionally, the motion planning of dual-manipulator systems is also investigated. The proposed single manipulator motion planner is extended to solve two unique decoupled motion planning problems. While existing APF techniques for the cooperative motion planning of multiple mobile robots are used as inspiration to develop a novel APF-based motion planner which successfully solves fully-cooperative dual-arm motion planning tasks.