# Mixed Reality Enhanced User Interactive Path Planning for Omnidirectional Mobile Robot
## 1 Introduction  
In recent years, with the improvements in production and life automation, the applications of mobile robots are becoming more and more extensive [1; 2; 3]. In different scenarios, mobile robots can play different roles, so it is necessary to design and develop robots according to specific user requirements. Therefore, their types and functions are increasingly rich. Efficient path planning is the core technology of a mobile robot, which enables it to move to the designated position in the environments and avoid obstacles safely [4; 5]. Different scenarios have different requirements for path planning. Shorter paths can be chosen or safer distances can be considered. In addition, the research and development of much equipment make the control methods of mobile robots more and more abundant, which can meet the interactive tasks of operators in different working environments [6; 7]. As a kind of mobile robot, the omnidirectional mobile robot (OMR) has attracted people's attention due to its unique characteristics. It has four Mecanum wheels, each of which has an axis of rotation at a 45-degree angle to the robot body and is driven by a separate motor [8; 9]. Therefore, it can carry out lateral and oblique movements that cannot be completed by other constrained mobile robots, due to the advantages of omnidirectional movement and good movement in a limited space [10; 11]. The OMR is used in many practical situations, such as the omnidirectional mobile wheelchair [12]. Its motion characteristics make it of high research significance. For path planning, there are a lot of research results. Current methods include the A* algorithm [13], D* algorithm [14], artificial potential field method [15], vector field histogram* (VFH*) algorithm [16], etc. The VFH* algorithm is a combination of the improved VFH obstacle avoidance algorithm and the A* algorithm. VFH is an object-oriented obstacle avoidance method [17]. Specifically, the vector field histogram is used to represent the surrounding environment of the mobile robot, and the next movement direction is selected according to the cost function of each direction [18]. But this approach sometimes fails to choose the right direction. Therefore, [PERSON] and [PERSON] proposed a new VFH* algorithm in [16] to minimize the cost and seek the global optimal solution with heuristic functions. The path planning algorithm used in this paper is an improvement on the original VFH * algorithm. These improvements make the method better applicable to the system proposed in this paper. Virtual reality (VR) technology simulates a virtual environment by computer to give people a sense of immersion and to allow operators to interact with virtual objects [19; 20; 21]. At present, it has been widely used in many fields. Compared with traditional robot control methods, VR can expand the experimental operation space, eliminate security risks in human-robot interaction, and simplify the experimental debugging process [22; 23]. Mixed reality (MR) is a technology different from VR, which can superimpose virtual objects into the real environment [24; 25]. It also has an important application in robot control [26]. With more and more research on VR and MR, it will certainly bring new developments to many scientific technologies, including robot control. At present, a lot of research has focused on VR with robot control. In [27], VR control of robot movement is mentioned. The control information is combined with the robot's motion through the Internet, which could be used as a separate simulation system or parallel control with the real motion system. There is also a system for real-time monitoring and networked control of remote robots, which can collect various data about robot operations and complete various assembly tasks [28]. Besides, the robot is controlled by the designed VR technology, and the robot's navigation function is completed in the server, to realize a remote navigation system suitable for simple remote robots [29].