Unmanned aerial vehicles(UAVs) are increasingly considered in safe autonomous navigation systems to explore unknown environments where UAVs are equipped with multiple sensors to perceive the surroundings. However, how...Unmanned aerial vehicles(UAVs) are increasingly considered in safe autonomous navigation systems to explore unknown environments where UAVs are equipped with multiple sensors to perceive the surroundings. However, how to achieve UAVenabled data dissemination and also ensure safe navigation synchronously is a new challenge. In this paper, our goal is minimizing the whole weighted sum of the UAV’s task completion time while satisfying the data transmission task requirement and the UAV’s feasible flight region constraints. However, it is unable to be solved via standard optimization methods mainly on account of lacking a tractable and accurate system model in practice. To overcome this tough issue,we propose a new solution approach by utilizing the most advanced dueling double deep Q network(dueling DDQN) with multi-step learning. Specifically, to improve the algorithm, the extra labels are added to the primitive states. Simulation results indicate the validity and performance superiority of the proposed algorithm under different data thresholds compared with two other benchmarks.展开更多
The indoor robots are expected to complete metric navigation tasks safely and efficiently in complex environments, which is the essential prerequisite for accomplishing other high-level operation tasks. 2 D occupancy ...The indoor robots are expected to complete metric navigation tasks safely and efficiently in complex environments, which is the essential prerequisite for accomplishing other high-level operation tasks. 2 D occupancy grid maps are sufficient to support the robots in avoiding all obstacles in the environments during navigation. However, the maps based on normal laser scans only reflect a horizontal slice of the environment, which may cause the problem of some obstacles missing or misinterpreting their exact boundaries,thereby threatening the safety and efficiency of robot navigation. This paper presents a 2 D mapping method based on virtual laser scans to provide a more comprehensive representation of obstacles for indoor robot navigation. The resulting maps can accurately represent the top-down projected contours of all obstacles no matter where their vertical positions are. The virtual laser scans are initially generated from raw data of an RGB-D camera based on the filtering, projection, and polar-coordinate scanning. The scans are fed directly to the laser-based simultaneous localization and mapping(SLAM) algorithms to update the current map and robot position. Two auxiliary strategies are proposed to further improve the quality of maps by reducing the impact of the narrow field of view and the blind zone of the RGB-D camera on the observations. In this paper, the improved virtual laser generation method makes the extracted 2 D observations fit the laser-based SLAM algorithms, and two auxiliary strategies are novel ways to improve map quality. The generated maps can reflect the comprehensive obstacle information in indoor environments with good accuracy. The comparative experiments are carried out based on four simulation scenarios and three real-world scenarios to prove the effectiveness of our 2 D mapping method.展开更多
基金supported by the National Natural Science Foundation of China (No. 61931011)。
文摘Unmanned aerial vehicles(UAVs) are increasingly considered in safe autonomous navigation systems to explore unknown environments where UAVs are equipped with multiple sensors to perceive the surroundings. However, how to achieve UAVenabled data dissemination and also ensure safe navigation synchronously is a new challenge. In this paper, our goal is minimizing the whole weighted sum of the UAV’s task completion time while satisfying the data transmission task requirement and the UAV’s feasible flight region constraints. However, it is unable to be solved via standard optimization methods mainly on account of lacking a tractable and accurate system model in practice. To overcome this tough issue,we propose a new solution approach by utilizing the most advanced dueling double deep Q network(dueling DDQN) with multi-step learning. Specifically, to improve the algorithm, the extra labels are added to the primitive states. Simulation results indicate the validity and performance superiority of the proposed algorithm under different data thresholds compared with two other benchmarks.
基金supported by National Natural Science Foundation of China(Nos.U1813215 and 61773239)the Taishan Scholars Program of Shandong Province(No.ts201511005)。
文摘The indoor robots are expected to complete metric navigation tasks safely and efficiently in complex environments, which is the essential prerequisite for accomplishing other high-level operation tasks. 2 D occupancy grid maps are sufficient to support the robots in avoiding all obstacles in the environments during navigation. However, the maps based on normal laser scans only reflect a horizontal slice of the environment, which may cause the problem of some obstacles missing or misinterpreting their exact boundaries,thereby threatening the safety and efficiency of robot navigation. This paper presents a 2 D mapping method based on virtual laser scans to provide a more comprehensive representation of obstacles for indoor robot navigation. The resulting maps can accurately represent the top-down projected contours of all obstacles no matter where their vertical positions are. The virtual laser scans are initially generated from raw data of an RGB-D camera based on the filtering, projection, and polar-coordinate scanning. The scans are fed directly to the laser-based simultaneous localization and mapping(SLAM) algorithms to update the current map and robot position. Two auxiliary strategies are proposed to further improve the quality of maps by reducing the impact of the narrow field of view and the blind zone of the RGB-D camera on the observations. In this paper, the improved virtual laser generation method makes the extracted 2 D observations fit the laser-based SLAM algorithms, and two auxiliary strategies are novel ways to improve map quality. The generated maps can reflect the comprehensive obstacle information in indoor environments with good accuracy. The comparative experiments are carried out based on four simulation scenarios and three real-world scenarios to prove the effectiveness of our 2 D mapping method.
