Coverage path planning is a major application for mobile robots, which requires robots to move along a planned path to cover the entire map. For large-scale tasks, multirobot systems offer significant advantages. In this letter, we propose Turn-minimizing Multirobot Spanning Tree Coverage Star (TMSTC*), an improved multirobot coverage path planning (mCPP) algorithm based on MSTC*. Our algorithm partitions the map into minimum bricks as tree branches, transforming the problem into finding the maximum independent set of a bipartite graph. We then use a greedy strategy to connect bricks and form a tree, aiming to minimize the number of turns of the corresponding circumnavigating coverage path. Our experimental results show that our approach enables multiple robots to make fewer turns, resulting in faster completion of coverage tasks compared to other popular algorithms.

The optimal design objectives of the microwave absorbing (MA) materials are high absorption, wide bandwidth, light weight and thin thickness. However, it is difficult for single-layer MA materials to meet all of these requirements. Constructing multi-layer structure absorbing coating is an important means to improve performance of MA materials. The carbon-based nanocomposites are excellent MA materials. In this paper, genetic algorithm (GA) and artificial bee colony algorithm (ABC) are used to optimize the design of multi-layer materials. We selected ten kinds of materials to construct the multi-layer absorbing material and optimize the performance. Two algorithms were applied to optimize the two-layer MA material with a total thickness of 3 mm, and it was found that the optimal bandwidth was 8.12 GHz and reflectivity was −53.4 dB. When three layers of MA material with the same thickness are optimized, the ultra-wide bandwidth was 10.6 GHz and ultra-high reflectivity was −84.86 dB. The bandwidth and reflectivity of the optimized material are better than the single-layer material without optimization. Comparing the GA and the ABC algorithm, the ABC algorithm can obtain the optimal solution in the shortest time and highest efficiency. At present, no such results have been reported.

Due to budgetary constraints, indoor navigation typically employs two-dimensional (2D) LiDAR rather than 3D LiDAR. However, the utilization of 2D LiDAR in simultaneous localization and mapping (SLAM) frequently encounters challenges related to motion degeneracy, particularly in geometrically similar environments. To address this problem, this paper proposes a robust, accurate, and multi-sensor-fused 2D LiDAR SLAM system specifically designed for indoor mobile robots. To commence, the original LiDAR data undergoes meticulous processing through point and line extraction. Leveraging the distinctive characteristics of indoor environments, line–line constraints are established to complement other sensor data effectively, thereby augmenting the overall robustness and precision of the system. Concurrently, a tightly-coupled front-end is created, integrating data from the 2D LiDAR, inertial measurement unit, and wheel odometry, thus enabling real-time state estimation. Building upon this solid foundation, a novel global feature point matching-based loop closure detection algorithm is proposed. This algorithm proves highly effective in mitigating front-end accumulated errors and ultimately constructs a globally consistent map. The experimental results indicate that our system fully meets real-time requirements. When compared to cartographer, our system not only exhibits lower trajectory errors but also demonstrates stronger robustness, particularly in degeneracy problem. We open source our methods here: https://github.com/LittleDang/2DLIW-SLAM.