Search Results (3)

Garbage sweepers are crucial to municipal cleaning; however, they frequently encounter two significant challenges during their operations: overflowing rubbish storage compartments and vacuum duct clogging. The conventional method of monitoring overflowing refuse and clogging vacuum ducts is based on manual labour, which is inefficient and susceptible to error. Consequently, this investigation suggests an automated monitoring approach that is predicated on the integration of wind speed sensors and ultrasonic sensors. The ultrasonic sensors assess the blockage status by monitoring the height of the accumulation of rubbish in real time, while the wind speed sensors monitor the change in wind speed. The experimental results indicate that the system is an effective solution for monitoring the refuse intake status of intelligent sweepers, significantly reducing manual intervention and improving the operational efficiency of the equipment. Additionally, the system is accurate and reliable. Full article

Replacing traditional manual sweeping with unmanned sweepers in closed parks can not only greatly reduce labor costs, but also improve sweeping efficiency. Efficient path planning is the key technology for unmanned sweepers to complete the sweeping task. Existing unmanned sweepers are often based on fixed path sweeping or completely traversing the sweeping mode, this mode does not have the environmental adaptability, in the actual sweeping is often high energy cost, and sweeping is not complete. In this paper, an environment-adaptive sweeping path planning method is proposed to improve the sweeping intelligence and environmental adaptability of unmanned sweepers, reduce the energy consumption of sweeping and improve the efficiency of sweeping. Specifically, in this paper, we first use YOLOv5 to complete the accurate identification of individual garbage and obstacles in the road, and then work with LIDAR and Gaussian Mixture Model(GMM) to remove redundant targets. We also propose a Permutation Entropy(PE) value-based discrimination method to complete the target distribution posture analysis of each complex garbage pile. Finally, the traditional path planning problem is transformed into a combinatorial optimization problem of garbage areas, and a sweeping path accurate method based on Simulated Annealing(SA) algorithm is proposed. Through comprehensive theoretical analysis and simulation study, the optimality and effectiveness of the proposed method are proved by comparing A star and Coverage Path Planning(CPP) algorithms in a variety of experimental scenarios. Full article

Road sweepers are ubiquitous machines that help preserve our cities cleanliness and health by collecting road garbage and sweeping out dirt from our streets and sidewalks. They are often very mechanical instruments, needing to operate in harsh conditions dealing with all sorts of abandoned trash and natural garbage. They are usually composed of rotating brushes, collector belts and bins, and sometimes water or air streams. All of these mechanical tools are usually high in power demand and strongly subject to wear and tear. Moreover, due to the simple working logic often implied by these cleaning machines, these tools work in an “always on”/“max power” state, and any further regulation is left to the pilot. Therefore, adding artificial intelligence able to correctly operate these tools in a semi-automatic way would be greatly beneficial. In this paper, we propose an automatic road garbage detection system, able to locate with great precision most types of road waste, and to correctly instruct a road sweeper in order to handle them. With this simple addition to an existing sweeper, we will be able to save more than 80% electrical power currently absorbed by the cleaning systems and reduce by the same amount brush weariness (prolonging their lifetime). This is done by choosing when to use the brushes and when not to, with how much strength, and where. The only hardware components needed by the system will be a camera and a PC board able to read the camera output (and communicate via CanBus). The software of the system will be mainly composed of a deep neural network for semantic segmentation of images, and a real-time software program to control the sweeper actuators with the appropriate timings. To prove the claimed results, we run extensive tests onboard of such a truck, as well as benchmark tests for accuracy, sensitivity, specificity and inference speed of the system. Full article