Balanced Order Batching with Task-Oriented Graph Clustering

Balanced order batching problem (BOBP) arises from the process of warehouse picking in Cainiao, the largest logistics platform in China. Batching orders together in the picking process to form a single picking route, reduces travel distance. The reason for its importance is that order picking is a labor intensive process and, by using good batching methods, substantial savings can be obtained. The BOBP is a NP-hard combinational optimization problem and designing a good problem-specific heuristic under the quasi-real-time system response requirement is non-trivial. In this paper, rather than designing heuristics, we propose an end-to-end learning and optimization framework named Balanced Task-orientated Graph Clustering Network (BTOGCN) to solve the BOBP by reducing it to balanced graph clustering optimization problem. In BTOGCN, a task-oriented estimator network is introduced to guide the type-aware heterogeneous graph clustering networks to find a better clustering result related to the BOBP objective. Through comprehensive experiments on single-graph and multi-graphs, we show: 1) our balanced task-oriented graph clustering network can directly utilize the guidance of target signal and outperforms the two-stage deep embedding and deep clustering method; 2) our method obtains an average 4.57m and 0.13m picking distance ("m" is the abbreviation of the meter (the SI base unit of length)) reduction than the expert-designed algorithm on single and multi-graph set and has a good generalization ability to apply in practical scenario.Comment: 10 pages, 6 figure

CapsuleBot: A Novel Compact Hybrid Aerial-Ground Robot with Two Actuated-wheel-rotors

This paper presents the design, modeling, and experimental validation of CapsuleBot, a compact hybrid aerial-ground vehicle designed for long-term covert reconnaissance. CapsuleBot combines the manoeuvrability of bicopter in the air with the energy efficiency and noise reduction of ground vehicles on the ground. To accomplish this, a structure named actuated-wheel-rotor has been designed, utilizing a sole motor for both the unilateral rotor tilting in the bicopter configuration and the wheel movement in ground mode. CapsuleBot comes equipped with two of these structures, enabling it to attain hybrid aerial-ground propulsion with just four motors. Importantly, the decoupling of motion modes is achieved without the need for additional drivers, enhancing the versatility and robustness of the system. Furthermore, we have designed the full dynamics and control for aerial and ground locomotion based on the bicopter model and the two-wheeled self-balancing vehicle model. The performance of CapsuleBot has been validated through experiments. The results demonstrate that CapsuleBot produces 40.53% less noise in ground mode and consumes 99.35% less energy, highlighting its potential for long-term covert reconnaissance applications.Comment: 7 pages, 10 figures, submitted to 2024 IEEE International Conference on Robotics and Automation (ICRA). This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Build and control a novel hybrid flying-moving robot

In this dissertation, the dynamic model and control of DoubleBee, a novel hybrid flying-moving vehicle consisting of two propellers mounted on tilting servo motors and two motor-driven wheels, are presented. DoubleBee exploits the high energy efficiency of a bicopter configuration in aerial mode, and enjoys the low power consumption of a two-wheel self-balancing robot on the ground. Furthermore, the propeller thrusts act as additional control inputs on the ground, enabling a novel decoupled control scheme where the attitude of the robot is controlled using thrusts and the translational motion is realized using wheels. A prototype of DoubleBee is constructed using commercially available components. The power efficiency and the control performance of the robot are verified through comprehensive experiments. Challenging tasks in indoor and outdoor environments demonstrate the capability of DoubleBee to traverse unstructured environments, fly over and move under barriers, and climb steep and rough terrains.Master of Science (Computer Control and Automation

Method of Association Rules Mining and Its Application in Analysis of Seawater Samples

This paper aims to set up new rules for processing seawater quality monitoring data collected by photoelectric sensor network, and mine out the useful information contained in the data. For this purpose, the immune algorithm was introduced to the classical genetic algorithm, the fitness function was designed, and the crossover and mutation probabilities were adjusted, thus creating the adaptive immune genetic algorithm (IIGA). The new algorithm was described in details and applied in an actual case. Through the comparison between the IIGA, IGA and apriori algorithms, the author concluded that the IIGA not only shortened the mining time, but also ensured the operation accuracy. The research findings are of great importance to the association rules mining in various fields.</span