The Stewart platform (SP) is a parallel closed‐kinematic chain robotic mechanism that is capable of providing high structural and positional rigidity. Because of its unique capability, the platforms have been employed in many control engineering applications such as simulator shakers, robotic manipulators, etc. However, a main problem often found in the implementation of a real‐time controller for the platform is the lack of an efficient algorithm for solving its highly nonlinear forward kinematic transformation (FKT), where one seeks to find the translational and orientational altitudes of the moveable platform from knowing the lengths of the platform linkages. This article describes two new direct and exact methods for computing the translational and rotational displacements of an SP by employing extra transnational displacement sensors (TDSs), in addition to the existing TDSs for the six links of the SP. The key for the approach lies in knowing where to employ the TDSs for determining positional vectors of strategic platform locations. By taking advantage of a tetrahedral geometry, closed‐form solutions for the FKT can then be derived and directly evaluated. The new methods produce accurate solutions with only minimal computation necessary. The advantages and disadvantages of the proposed methods are discussed and compared to an existing method. The exact methods are being investigated for an on‐line implementation of a nonlinear adaptive control system and redundancy scheme for a 25‐ton Stewart platform‐based Crew Station/Turret Motion Base Simulator (CS/TMBS) at the U.S. Army Tank‐Automotive Command (TACOM).

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Multi-robot exploration and mapping studies have demonstrated that it is often more efficient to explore unknown areas in parallel rather than with a single agent. However, the question of how to integrate individual maps into a consistent global map remains an open research area. This problem, known as map merging, comprises the establishment of a frame of reference for multiple mobile robots, the identification of regions of map overlap, and the integration of individual maps to produce a global result. In this work, we build a hybrid map which integrates occupancy grid and feature data to solve this problem. This integrated representation permits fast and effective map merging. Experimental results are presented that demonstrate algorithm performance in a realistic scenario.