The efficiency of energy usage applied to robots that implement autonomous duties such as floor cleaning depends crucially on the adopted path planning strategies. Energy-aware for complete coverage path planning (CCPP) in the reconfigurable robots raises interesting research, since the ability to change the robot’s shape needs the dynamic estimate energy model. In this paper, a CCPP for a predefined workspace by a new floor cleaning platform (hTetro) which can self-reconfigure among seven tetromino shape by the cooperation of hinge-based four blocks with independent differential drive modules is proposed. To this end, the energy consumption is represented by travel distances which consider operations of differential drive modules of the hTetro kinematic designs to fulfill the transformation, orientation correction and translation actions during robot navigation processes from source waypoint to destination waypoint. The optimal trajectory connecting all pairs of waypoints on the workspace is modeled and solved by evolutionary algorithms of TSP such as Genetic Algorithm (GA) and Ant Optimization Colony (AC) which are among the well-known optimization approaches of TSP. The evaluations across several conventional complete coverage algorithms to prove that TSP-based proposed method is a practical energy-aware navigation sequencing strategy that can be implemented to our hTetro robot in different real-time workspaces. Moreover, The CCPP framework with its modulation in this paper allows the convenient implementation on other polynomial-based reconfigurable robots.

Offshore aquaculture fish farming faces labor shortage, safety, productivity and high operating cost issues. Unmanned underwater vehicles (UUVs) are being deployed to mitigate these issues. One of their applications is the fish net-pen visual inspection. This paper aims to develop and simulate with high-fidelity several trajectory tracking control schemes for a UUV to visually inspect a fish net-pen in a standard task scenario in offshore aquaculture under 0.0m/s, 0.5m/s and 0.9m/s underwater current disturbances. Three controllers, namely (1) Proportional-Derivative control with restoring force & moment compensation (Compensated-PD), (2) Proportional-Integral-Derivative control with restoring force & moment compensation (Compensated-PID), and (3) computed torque (or) inverse dynamics control (CTC/IDC) were conducted on a 6 degrees-of-freedom (DoF) BlueROV2 Heavy Configuration dealing with 12 error states (pose and twist). A standard task scenario for the controllers was formulated based on the Blue Endeavour project of the New Zealand King Salmon company located 5 kilometres due north of Cape Lambert, in northern Marlborough. This simulated experimental study gathered and applied many available and physically quantifiable parameters of the fish farm and a UUV called BlueROV2 Heavy Configuration. Results show that while utilizing the minimum thrust, CTC/IDC outperforms Compensated-PID and Compensated-PD in overall trajectory tracking under different underwater current disturbances. Numerical results measured with root-mean-square-error (RMSE), mean-absolute-error (MAE) and root-sum-squared (RSS) are reported for comparison, and simulation results in the form of histograms, bar charts, plots, and video recordings are provided. Future work will explore into advanced controllers, with a specific emphasis on energy-optimal control schemes, accompanied by comprehensive stability and robustness analyses applied to linear and nonlinear UUV models.

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.

False-ceiling inspection is a critical factor in pest-control management within a built infrastructure. Conventionally, the false-ceiling inspection is done manually, which is time-consuming and unsafe. A lightweight robot is considered a good solution for automated false-ceiling inspection. However, due to the constraints imposed by less load carrying capacity and brittleness of false ceilings, the inspection robots cannot rely upon heavy batteries, sensors, and computation payloads for enhancing task performance. Hence, the strategy for inspection has to ensure efficiency and best performance. This work presents an optimal functional footprint approach for the robot to maximize the efficiency of an inspection task. With a conventional footprint approach in path planning, complete coverage inspection may become inefficient. In this work, the camera installation parameters are considered as the footprint defining parameters for the false ceiling inspection. An evolutionary algorithm-based multi-objective optimization framework is utilized to derive the optimal robot footprint by minimizing the area missed and path-length taken for the inspection task. The effectiveness of the proposed approach is analyzed using numerical simulations. The results are validated on an in-house developed false-ceiling inspection robot—Raptor—by experiment trials on a false-ceiling test-bed.

Routine rodent inspection is essential to curbing rat-borne diseases and infrastructure damages within the built environment. Rodents find false ceilings to be a perfect spot to seek shelter and construct their habitats. However, a manual false ceiling inspection for rodents is laborious and risky. This work presents an AI-enabled IoRT framework for rodent activity monitoring inside a false ceiling using an in-house developed robot called “Falcon”. The IoRT serves as a bridge between the users and the robots, through which seamless information sharing takes place. The shared images by the robots are inspected through a Faster RCNN ResNet 101 object detection algorithm, which is used to automatically detect the signs of rodent inside a false ceiling. The efficiency of the rodent activity detection algorithm was tested in a real-world false ceiling environment, and detection accuracy was evaluated with the standard performance metrics. The experimental results indicate that the algorithm detects rodent signs and 3D-printed rodents with a good confidence level.