## 1 Introduction
The review of automated solutions for belt conveyor inspection and maintenance in [1; 7; 12] presents several approaches that were tested and can be mainly divided into three groups. * The direct approach in which sensors are mounted along the conveyor. Some solutions within this approach use discrete or distributed sensors. In the first case, the sensors are either integrated with the idler rolls or mounted on the idler frames. The sensors communicate using Internet of Things (IoT) technology, such as a mesh network. This approach allows for the precise, continuous monitoring of each idler's state. In the latter case, a fiber optic cable is installed along the conveyor for distributed acoustic sensing of overheating [13] or general damage of idlers [14]. However, due to the length of the conveyors and the sheer number of the idlers, this approach may not be the most economically efficient. * The second approach uses the belt as a transport medium for sensors. Examples include solutions where sensors are integrated with the belt (e.g., Honeywell BeltAIS) or IMU-based sensor devices inserted into transported material [15]. The measurements are indirect, as they measure the belt's reaction to the idler rollers' states, making them  
Figure 1: Idler supports of typical belt conveyors: (**a**) [4], (**b**) [5]. less precise than direct observations of the rollers. On the other hand, employing a single measurement unit for multiple idlers reduces the overall cost of monitoring. * The third approach aims to combine the advantages of direct measurement with the cost effectiveness of a single sensor unit by mounting sensors on a mobile platform. Within this approach, various solutions have been proposed. Some use a rail along the conveyor that transports a sensor module [16] or a track with an inspection robot on it, with either an RGB camera to detect belt displacement [17] or an audio signal and infrared image sensors to detect idler malfunction [18]. Others employ various mobile platforms for the transportation and operation of sensors. For example, Machinery Automation and Robotics (currently Scott) [1] presented the Robotic Idler Replacement System--a tracked platform with an industrial manipulator capable of inspecting and replacing idler rolls. [PERSON] et al. [19] introduced a manipulator mounted on a four-wheel platform for environmental monitoring, which cooperates with an Internet of Things (IoT) sensor network and is capable of autonomous driving. Among platforms investigated for carrying sensor modules are four-wheeled robots [20; 21], legged robots [22], and a hybrid wheeled-tracked mobile robot [23]. Furthermore, some researchers explore the use of unmanned aerial vehicles (UAVs) to create an integrated monitoring and inspection system employing quadrocopters [3; 24] or combined systems of mobile robots with UAV landing pads [25]. Most of these platforms rely on teleoperation. Even in cases when the measurement process with placement of the sensor next to an idler roll is automated, the platform's motion is usually controlled by the human operator. This is caused by the lack of sufficiently reliable localization, which is crucial for an autonomous robot to move along the predefined paths and execute tasks in set locations. The comparison of approaches presented in [12] emphasizes the usability of mobile robots due to their precision and ability to carry versatile sensors. At the same time, navigation and mobility are indicated as the main challenges of such platforms. In environments where precise absolute localization systems are not available, robots cannot rely on predefined motion plans. Instead, on-board sensors must be used to drive a robot to expected destinations and to aim its sensory system towards the measured objects. Currently, mobile industrial robots are frequently equipped with Light Detection and Ranging (LIDAR) sensors to scan their surroundings using infrared laser beams.