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4. Q. Bi, X. Zhang, J. Wen, Z. Pan, S. Zhang, R. Wang, J Yuan. CURE: A hierarchical framework for Multi-Robot autonomous exploration inspired by centroids of unknown regions, IEEE Transactions on Automation Science and Engineering, 2024, 21(3): 3773-3786.

通信作者： 张雪波  南开大学

摘要：In this paper, a novel multi-robot autonomous exploration approach CURE is proposed based on dynamic Voronoi diagrams and centroids of unknown connected regions. Compared with existing approaches, the novelty of this work is twofold: 1) Dynamic Voronoi diagram is used for partition of the space being explored to improve the efficiency of multi-robot exploration, and then a new parameter-insensitive utility function is elaborately designed to evaluate the information of centroids, which helps guide the robot to unknown regions. 2) A hierarchical framework consisting of global and local exploration windows for detecting centroids is designed, wherein the global exploration window is activated to find centroids to guide the robot exploration when there are no centroids in any one local exploration window. We validate the feasibility and exploration efficiency of the proposed approach in various complex simulation scenarios and challenging real-world tasks. All test results show that the exploration time consumption and path cost are reduced by up to 50.7% and 34.4%, respectively, compared with an advanced RRT-based multi-robot exploration approach.

5. Z. Song, X. Zhang, T. Li, S. Zhang, Y. Wang, J Yuan. IR-VIO: Illumination-robust visual-inertial odometry based on adaptive weighting algorithm with two-layer confidence maximization, IEEE/ASME Transactions on Mechatronics (T-MECH), 2023, 28(4): 1920-1929.

通信作者： 张雪波  南开大学

摘要：Illumination change, image blur, and fast motion dramatically decrease the performance of visual-inertial navigation systems (VINS). This article presents a new illumination-robust visual-inertial odometry (IR-VIO) based on adaptive weighting algorithm with two-layer confidence maximization. First, to prevent the VIO performance degradation caused by poor image quality in complex scenes and ignoring the confidence differences of feature points, we develop a novel adaptive weighting algorithm on the multisensor layer and visual feature layer to better fuse multisensor information and maximize the overall confidence of VIO. Second, to solve the problems of image feature tracking difficulty and excessive image noise in illumination-changing scenes, an image enhancement algorithm is introduced to enhance consecutive images to the same brightness level, while a block noise removal algorithm with constraint protection mechanism is proposed to dynamically remove noise points. Finally, experimental results in the public dataset and real-world environments demonstrate that IR-VIO has superior performance in terms of accuracy and robustness compared with the state-of-the-art methods.

6. J. Wen, X. Zhang, H. Gao, J. Yuan, Y. Fang. E3MoP: efficient motion planning based on heuristic-guided motion primitives pruning and path optimization with sparse-banded structure, IEEE Transactions on Automation Science and Engineering, 2022, 19(4): 2762-2775.

通信作者： 张雪波  南开大学

摘要：To solve the autonomous navigation problem in complex environments, an efficient motion planning approach is newly presented in this paper. Considering the challenges from large-scale, partially unknown complex environments, a three-layer motion planning framework is elaborately designed, including global path planning, local path optimization, and time-optimal velocity planning. Compared with existing approaches, the novelty of this work is twofold: 1) a novel heuristic-guided pruning strategy of motion primitives is proposed and fully integrated into the state lattice-based global path planner to further improve the computational efficiency of graph search, and 2) a new soft-constrained local path optimization approach is proposed, wherein the sparse-banded system structure of the underlying optimization problem is fully exploited to efficiently solve the problem. We validate the safety, smoothness, flexibility, and efficiency of our approach in various complex simulation scenarios and challenging real-world tasks. It is shown that the computational efficiency is improved by 66.21% in the global planning stage and the motion efficiency of the robot is improved by 22.87% compared with the recent quintic Bézier curve-based state space sampling approach. We name the proposed motion planning framework E3MoP, where the number 3 not only means our approach is a three-layer framework but also means the proposed approach is efficient in three stages.

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