# Motion Planning for a Mobile Humanoid Manipulator Working in an Industrial Environment
###### Abstract
The method based on master-slave coordination such as the stationary, dual-arm, Baxter robot, working on an assembly line and completing peg-in-hole tasks, was presented in the work [12]. The use of highly redundant humanoid mobile manipulators was proposed in [13]. The authors designed the motion planning algorithms to carry out \"human-like\" autonomous navigation and manipulation tasks. The redundancy resolution scheme for humanoid manipulators, equipped with differential driven platforms, was presented in [14]. The validity of the algorithm was checked by simulating the robot, in tracing the global optimal path generated by the \(A^{*}\) (A-star) graph search algorithm. It is worth noting that research on humanoid robotic systems is not just limited to industrial applications. Such systems are also utilized as support for disabled and elderly people [15], medical assistants [16], and service robots for intelligent or smart homes [17]. The solutions obtained in these areas can also be used in industrial applications. The mobility of robotic systems can be realized in different ways. In the case of humanoid manipulators, the use of human-like bipedal platforms seems to be natural [18]. Unfortunately, such a solution causes many technical problems that have not been solved satisfactorily. Additionally, walking robots can be useful in difficult, natural conditions; however, in an industrial environment, the use of wheeled robots is more efficient because this allows higher velocity to be achieved in the robotic system. The choice of the drive type used is also important. Wheeled platforms can be divided into the holonomic type, which can move in any direction, since they have three degrees of freedom, and the nonholonomic type, which are systems whose movement is subject to the constraint of rolling without slipping. The motion planning methods for mobile robots presented above have concerned systems equipped with nonholonomic platforms, but it seems that in industrial conditions, the use of holonomic platforms is justified. Such systems, equipped with Mecanum-type wheels, for example, perform well on the even surface of a production or warehouse hall, and the lack of movement limitations makes it possible to move in a cluttered environment and significantly increases the usefulness of the robot. In this paper, the trajectory planning method for a humanoid dual-arm manipulator mounted on a holonomic wheeled mobile platform, inspired by the Rollin' Justin [19] robot, was considered. The main goal of this work was to present the general framework for the motion planning of such systems. In contrast to other solutions, presented in the literature, the comprehensive approach to the execution of the robot task in an industrial environment was proposed. Solving the problem of the handling of lengthy objects, while simultaneously gripping such objects at several points, is something of a priority and was taken into account. In this case, the task of the robot, in the general form, can be divided into three phases, viz., move-to-grip, where the robot moves towards the object, moving the object to a specified location, and precise manipulation along a prescribed path. The proposed approach allows the motion planning problem to be resolved in all three phases. Moreover, in the presented method, the series of constraints, resulting from the mechanical properties of the mechanism and the boundary constraints resulting from the task, was considered. The constraints imposed on the mechanical limits of joint angles, as well as the constraints connected with potential collisions among the robot, its environment, the object being handled, and the inner collisions between the robot arms were taken into account, in particular. Additionally, motions were planned in order to maximize the instantaneous manipulability measure, in order to avoid manipulator singularities during movement and to ensure the high dexterity of the robot at the end of each phase of a particular task.