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Integration technologies of artificial intelligence (AI) and autonomous vehicles play important roles in intelligent transportation systems (ITS). In order to achieve better logistics distribution efficiency, this paper proposes an intelligent actuator of an indoor logistics system by fusing multiple involved sensors. Firstly, an actuator based on a four-wheel differential chassis is equipped with sensors, including an RGB camera, a lidar and an indoor inertial navigation system, by which autonomous driving can be realized. Secondly, cross-floor positioning can be realized by multi-node simultaneous localization and mappings (SLAM) based on the Cartographer algorithm Thirdly the actuator can communicate with elevators and take the elevator to the designated delivery floor. Finally, a novel indoor route planning strategy is designed based on an A* algorithm and genetic algorithm (GA) and an actual building is tested as a scenario. The experimental results have shown that the actuator can model the indoor mapping and develop the optimal route effectively. At the same time, the actuator displays its superiority in detecting the dynamic obstacles and actively avoiding the collision in the indoor scenario. Through communicating with indoor elevators, the final delivery task can be completed accurately by autonomous driving.
Pangwei Wang; Yunfeng Wang; Xu Wang; Ying Liu; Juan Zhang. An Intelligent Actuator of an Indoor Logistics System Based on Multi-Sensor Fusion. Actuators 2021, 10, 120 .
AMA StylePangwei Wang, Yunfeng Wang, Xu Wang, Ying Liu, Juan Zhang. An Intelligent Actuator of an Indoor Logistics System Based on Multi-Sensor Fusion. Actuators. 2021; 10 (6):120.
Chicago/Turabian StylePangwei Wang; Yunfeng Wang; Xu Wang; Ying Liu; Juan Zhang. 2021. "An Intelligent Actuator of an Indoor Logistics System Based on Multi-Sensor Fusion." Actuators 10, no. 6: 120.
It is agreed that connected vehicle technologies have broad implications to traffic management systems. In order to alleviate urban congestion and improve road capacity, this paper proposes a multilane spatiotemporal trajectory optimization method (MSTTOM) to reach full potential of connected vehicles by considering vehicular safety, traffic capacity, fuel efficiency, and driver comfort. In this MSTTOM, the dynamic characteristics of connected vehicles, the vehicular state vector, the optimized objective function, and the constraints are formulated. The method for solving the trajectory problem is optimized based on Pontryagin’s maximum principle and reinforcement learning (RL). A typical scenario of intersection with a one-way 4-lane section is measured, and the data within 24 hours are collected for tests. The results demonstrate that the proposed method can optimize the traffic flow by enhancing vehicle fuel efficiency by 32% and reducing pollutants emissions by 17% compared with the advanced glidepath prototype application (GPPA) scheme.
Pangwei Wang; Yunfeng Wang; Hui Deng; Mingfang Zhang; Juan Zhang. Multilane Spatiotemporal Trajectory Optimization Method (MSTTOM) for Connected Vehicles. Journal of Advanced Transportation 2020, 2020, 1 -15.
AMA StylePangwei Wang, Yunfeng Wang, Hui Deng, Mingfang Zhang, Juan Zhang. Multilane Spatiotemporal Trajectory Optimization Method (MSTTOM) for Connected Vehicles. Journal of Advanced Transportation. 2020; 2020 ():1-15.
Chicago/Turabian StylePangwei Wang; Yunfeng Wang; Hui Deng; Mingfang Zhang; Juan Zhang. 2020. "Multilane Spatiotemporal Trajectory Optimization Method (MSTTOM) for Connected Vehicles." Journal of Advanced Transportation 2020, no. : 1-15.