Design and Experimental Evaluation of an Aerial Solution for Visual Inspection of Tunnel-like Infrastructures

Current railway tunnel inspections rely on expert operators performing a visual examination of the entire infrastructure and manually annotating encountered defects. Automatizing the inspection and maintenance task of such critical and aging infrastructures has the potential to decrease the associated costs and risks. Contributing to this aim, the present work describes an aerial robotic solution designed to perform autonomous inspections of tunnel-like infrastructures. The proposed robotic system is equipped with visual and thermal sensors and uses an inspection-driven path planning algorithm to generate a path that maximizes the quality of the gathered data in terms of photogrammetry goals while optimizing the surface coverage and the total trajectory length. The performance of the planning algorithm is demonstrated in simulation against state-of-the-art methods and a wall-following inspection trajectory. Results of a real inspection test conducted in a railway tunnel are also presented, validating the whole system operation.

Reinforcement Learning Path Planning Method with Error Estimation

Path planning is often considered as an important task in autonomous driving applications. Current planning method only concerns the knowledge of robot kinematics, however, in GPS denied environments, the robot odometry sensor often causes accumulated error. To address this problem, an improved path planning algorithm is proposed based on reinforcement learning method, which also calculates the characteristics of the cumulated error during the planning procedure. The cumulative error path is calculated by the map with convex target processing, while modifying the algorithm reward and punishment parameters based on the error estimation strategy. To verify the proposed approach, simulation experiments exhibited that the algorithm effectively avoid the error drift in path planning.

Intelligent Path Planning Approach for Autonomous Mobile Robot

This paper presents a new path planning algorithm for an autonomous mobile robot. It is desired that the robot reaches its goal in a known or partially known environment (e.g., a warehouse or an urban environment) and avoids collisions with walls and other obstacles. To this end, a new, efficient, simple, and flexible path finder strategy for the robot is proposed in this paper. With the proposed strategy, the optimal path from the robot’s current position to the goal position is guaranteed. The environment is represented as a grid-based map, which is then divided into a predefined number of subfields to reduce the number of required computations. This leads to a reduction in the load on the controller and allows a real-time response. To evaluate the flexibility and efficiency of the proposed strategy, several tests were simulated with environments of different sizes and obstacle distributions. The experimental results demonstrate the reliability and efficiency of the proposed algorithm.