# A Single RF Emitter-Based Indoor Navigation Method for Autonomous Service Robots
## 3 Autonomous Robot System Design  
Navigation for mobile platforms has been investigated extensively in the existing literature. In [17], a typical approach of combining computer vision, infrared range finders, and dynamic path planning is presented. In the context of industrial automation, [11] describes the use of 3D laser range finders, inertial measurement units (IMU), and a point cloud technique to dynamically map the environment and perform navigation. In [5], using laser range finders to detect artificial landmarks for localisation is investigated. Research such as [18] looks specifically at advancing long-term path planning in similar contexts. In order to achieve robust and accurate navigation towards a target object, we need hardware to support the goals of target localisation (identifying where the target is), robot localisation (identifying where the robot itself is), and environmental sensing (identifying where obstacles are). With this information, we are able to use software to generate a map of the environment, perform path planning, and determine how to control the robot's actuators through motion planning in order to move the robot towards the target destination. The interaction between these different modules is shown in Figure 1(a). Figure 1: A diagram showing a factory automation application where raw materials and products need to be fetched from and delivered to different locations according to production schedules. In this paper, we will present two versions of this system. In the preliminary (basic) navigation system, only the target localisation and motion planning modules are used, which are capable of navigating towards the target in simple scenarios but can become trapped in complex environments. In the full navigation system, all four modules are used so that the robot is capable of mapping the environment, constructing a long-term path plan to reach the final target, and manoeuvering to avoid obstacles in order to satisfy all requirements of the motivating scenario. The performance of these two navigation systems will be compared in Section 7. In terms of sensing hardware, our system primarily uses a single wireless emitter (on the target) and four receivers (on the mobile robot) to perform the key task of target localisation, and some environmental sensors (i.e., a laser range finder and contact sensors) are used for basic obstacle avoidance. A simple method of obstacle detection is available through contact or touch sensors: when the robot comes into contact with an obstacle, it is detected and the system can react accordingly (generally by reversing and marking the obstacle in a map). However, this approach requires the robot to move slowly so that it has time to stop if it encounters an obstacle, and is still risky as a failed detection can lead to the robot colliding with an obstacle. Generally, range finding is more appropriate as it is a passive form of sensing that does not require physical interaction with the obstacle. By using some form of wireless signal, the distance between the robot and an obstacle can be detected by processing the signal in some way. There are a variety of methods that determine the distance to an obstacle [3; 4]. The use of image processing with a camera allows for detailed information to be provided in a 3D space. Stereo vision can be used to find the depth of an area by comparing images of the environment from multiple points of view. In [19], a single camera is used in conjunction with odometry information from the mobile platform to compute depth information. However, vision-based methods are computationally expensive and may not be suited for embedded systems with limited computing resources available [4]. Alternative methods of range finding include sonar and laser range finding, which calculate the distance to an object based on the time of flight for an emitted signal to be reflected by the object and return to the emitter. Laser range finding provides higher resolution and accuracy than sonar for a greater initial cost of purchase.