Robots may face difficulty detecting all the required objects with their current vision in a search and rescue (SAR) operation in a disaster scenario. This can be due to a partial view of some objects from the whole disaster scenario, or the training model of the robots may not be perfect enough to recognize all the available objects. This research established a method for SAR robots to learn without human interaction by combining their own and other robots' knowledge. It assumes that all robots have a simple machine learning model that can detect objects from their whole perspective. Yolov8, trained with a custom dataset, has been used as the basic machine learning model in this study to detect the object. Two robots and one object are used for the proof of concept, with one robot seeing the object fully and the other partially. Partial view images were auto-labeled, and the fundamental machine learning model of the robot with partial view was retrained using auto-labeled data until a predetermined condition was met. Then the other robot in the environment received the final retrained model with partial view information. The suggested system was tested in numerous scenarios and usually works well.

A Novel Collaborative Knowledge Sharing and Self-Learning Framework for Robotic Systems in Search and Rescue Operations

Data collection and annotation is a time consuming and costly process, yet necessary for machine vision. Automation of construction equipment relies on seeing and detecting different objects in the vehicle’s surroundings. Construction equipment is commonly used to perform frequent repetitive tasks, which are interesting to automate. An example of such a task is the short-loading cycle, where the material is moved from a pile into the tipping body of a dump truck for transport. To complete this task, the wheel loader needs to have the capability to locate the tipping body of the dump truck. The machine vision system also allows the vehicle to detect unforeseen dangers such as other vehicles and more importantly human workers. In this work, we investigate the viability to perform semi-automatic annotation of video data using linear interpolation. The data is collected using scale-models mimicking a wheel-loaders approach towards a dump truck during the short-loading cycle. To measure the viability of this type of solution, the workload is compared to the accuracy of the model, YOLOv3. The results indicate that it is possible to maintain the performance while decreasing the annotation workload by about 95%. This is an interesting result for this application domain, as safety is critical and retaining the vision system performance is more important than decreasing the annotation workload. The fact that the performance seems to retain with a large workload decrease is an encouraging sign.

Semi-Automatic Video Frame Annotation for Construction Equipment Automation Using Scale-Models

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Semi-Automatic Video Frame Annotation for Construction Equipment Automation Using Scale-Models