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Mobile Manipulators are special cases of mobile robots that need a careful consideration when planning their motion. Due to their higher and changing centre of gravity caused by the position of the manipulator and its configuration, abrupt motion or change of direction can have dangerous consequences and the toppling of the whole mobile manipulator. Therefore, the motion planning process has to produce smooth and safe trajectories. Since the 1980’s mobile robot motion planning problems have been an important research topic that has attracted the attention of many researchers who worked extensively to obtain efficient methods to solve these problems. Such problems have been approached in two general ways: one approach has concentrated on solving motion planning problems by considering a previously known global environment or obstacles information and robot characteristics; the second approach has focused on planning motions by using local sensor information and robot characteristics. The first approach has been used extensively at a global planning level in robotics to plan trajectories from one environment location to another. This plan functions under the assumption of a perfectly controlled and modelled environment that in most situations does not exist. Unexpected obstacles, persons or moving elements make this approach difficult to utilize except in robustly controlled environments, such as in industrial manipulation environments (manufacturing). The second approach became evident in mobile robotics from the very beginning. A mobile robot’s environment presents unexpected objects which makes it impossible to use the first approach exclusively. The second approach produces local plans by using local sensor information to avoid obstacles. Obviously a local plan is a solution for a local scale problem and needs to be integrated with a global planner or with global information to guarantee the existence of a solution to the global problem. This method received different names such as collision avoidance methods, local planners, and navigation methods. To navigate in complex environments, an autonomous mobile robot needs to reach a compromise between the need for reacting to unexpected events and the need for efficient and optimized trajectories. Usually, path planning methods work in two steps: in the first

Sensor-based Global Planning for Mobile Manipulators Navigation Using Voronoi Diagram and Fast Marching