Completed area coverage planning (CACP) plays an essential role in various fields of robotics, such as area exploration, search, rescue, security, cleaning, and maintenance. Tiling robots with the ability to change their shape is a feasible solution to enhance the ability to cover predefined map areas with flexible sizes and to access the narrow space constraints. By dividing the map into sub-areas with the same size as the changeable robot shapes, the robot can plan the optimal movement to predetermined locations, transform its morphologies to cover the specific area, and ensure that the map is completely covered. The optimal navigation planning problem, including the least changing shape, shortest travel distance, and the lowest travel time while ensuring complete coverage of the map area, are solved in this paper. To this end, we propose the CACP framework for a tiling robot called hTrihex with three honeycomb shape modules. The robot can shift its shape into three different morphologies ensuring coverage of the map with a predetermined size. However, the ability to change shape also raises the complexity issues of the moving mechanisms. Therefore, the process of optimizing trajectories of the complete coverage is modeled according to the Traveling Salesman Problem (TSP) problem and solved by evolutionary approaches Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Hence, the costweight to clear a pair of waypoints in the TSP is defined as the required energy shift the robot between the two locations. This energy corresponds to the three operating processes of the hTrihex robot: transformation, translation, and orientation correction. The CACP framework is verified both in the simulation environment and in the real environment. From the experimental results, proposed CACP capable of generating the Pareto-optimal outcome that navigates the robot from the goal to destination in various workspaces, and the algorithm could be adopted to other tiling robot platforms with multiple configurations.
The maintenance of ship hulls involves a series of routine tasks during dry-docking that renews its life-time and operating efficiency. One such task is hull inspection, which is always seen as harmful for human operators and a time-consuming task. The shipping maintenance industries started using the robotic solutions in order to reduce the human risk. However, most of such robotic systems cannot operate fully autonomously due to the fact that it requires humans in the loop. On the other hand, an autonomous hull inspection robot, called Sparrow, is presented in this paper. The proposed robot is capable of navigating autonomously on the vertical metal surface and it could perform metal thickness inspection. This article summarizes the robot’s mechanical design, system control, autonomy, and the inspection module. We evaluated the robot’s performance by conducting experimental trials on three different metal plates that varied in thickness. The results indicate that the presented robot achieves significantly better locomotion while climbing, and it can autonomously measure the metal thickness, which significantly reduces the human efforts in real-time.
The cleaning robots are going through some significant development in recent years, driven by the greater market penetration and the demand for better cleaning performance. However, most of the robots have problems covering the total cleaning area given geometric limitations of the platforms in relation to the cleaning stage, given furniture and architecture. In this paper, we describe the hTrihex platform, which is a self-reconfigurable adaptive robot that attains three different configurations. The change in configuration gives rise to variation in the kinematic model, which in turn changes the behavior of robot locomotion. The change in configuration and the robot position is evaluated using the measurements from the on-board sensors like the encoders, LIDAR, and inertial measuring unit (IMU). These data are then utilized to develop a new cascade control strategy for motion control of h-Trihex. The control cascade comprises a robust backstepping controller in the outer loop for tackling the varying kinematic and a conventional PID in the inner loop for speed control. This cascade is appended by a PID controller, for correcting the wheel orientation after each configuration change. The proposed design assures asymptotic path tracking and satisfactory closed-loop performance, which are validated through numerical simulations and experiments.
Area coverage is a crucial factor for a robot intended for applications such as floor cleaning, disinfection, and inspection. Robots with fixed shapes could not realize an adequate level of area coverage performance. Reconfigurable robots have been introduced to overcome the limitations of fixed-shape robots, such as accessing narrow spaces and cover obstacles. Although state-of-the-art reconfigurable robots used for coverage applications are capable of shape-changing for improving the area coverage, the reconfiguration is limited to a few predefined shapes. It has been proven that the ability of reconfiguration beyond a few shapes can significantly improve the area coverage performance of a reconfigurable robot. In this regard, this paper proposes a novel robot model and a low-level controller that can facilitate the reconfiguration beyond a small set of predefined shapes and locomotion per instructions while firmly maintaining the shape. A prototype of a robot that facilitates the aim mentioned above has been designed and developed. The proposed robot model and controller have been integrated into the prototype, and experiments have been conducted considering various reconfiguration and locomotion scenarios. Experimental results confirm the validity of the proposed model and controller during reconfiguration and locomotion of the robot. Moreover, the applicability of the proposed model and controller for achieving high-level autonomous capabilities has been proven.
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