Adaptive Monte Carlo localization (AMCL) algorithm has a limited pose accuracy because of the nonconvexity of the laser sensor model, the complex and unstructured features of the working environment, the randomness of particle sampling, and the final pose selection problem. In this paper, an improved AMCL algorithm is proposed, aiming to build a laser radar‐based robot localization system in a complex and unstructured environment, with a LIDAR point cloud scan‐matching process after the particle score calculating process. The weighted mean pose of AMCL particle swarm is used as the initial pose of the scan matching process. The LIDAR point cloud is matched with the probability grid map from coarse to fine using the Gaussian‐Newton method, which results in more accurate poses. Moreover, the scan‐matching pose is added into the particle swarm as a high‐weight particle. So the particle swarm after resampling will be more concentrated in the correct position. The particle filter and the scan‐matching process form a closed loop, thus enhancing the localization accuracy of mobile robots. The experiment results demonstrate that the proposed improved AMCL algorithm is superior to the traditional AMCL algorithm in the complex and unstructured environment, by exploiting the high‐accuracy characteristic of scan matching while inheriting the stability of AMCL.

We address the problem of resilient consensus in second-order multiagent networks (MANs) with a time-varying set of malicious agents. Existing research typically assumes a static set of malicious agents. However, in real-world scenarios, network attacks and recoveries may lead to dynamic changes in the roles of agents. To overcome this challenge, a novel adaptive weighted subsequence reduction (AWSR) is proposed for resilient consensus in MANs with a dynamic malicious agent set. Based on the AWSR approach, a resilient consensus algorithm is designed for second-order MANs. Graph-theoretic conditions in the context of network robustness are derived to ensure resilient consensus in the considered MAN. We also extend the algorithm and analysis to MANs with communication delays. Finally, simulation experiments are conducted to demonstrate the effectiveness of the proposed algorithms.

In this paper, we investigate the algorithms for traversal exploration and path coverage of target regions using multiple agents, enabling the efficient deployment of a set of agents to cover a complex region. First, the original multi-agent path planning problem (mCPP) is transformed into several single-agent sub-problems, by dividing the target region into multiple balanced sub-regions, which reduces the explosive combinatorial complexity; subsequently, closed-loop paths are planned in each sub-region by the rapidly exploring random trees (RRT) algorithm to ensure continuous exploration and repeated visits to each node of the target region. On this basis, we also propose two improvements: for the corner case of narrow regions, the use of geodesic distance is proposed to replace the Eulerian distance in Voronoi partitioning, and the iterations for balanced partitioning can be reduced by more than one order of magnitude; the Dijkstra algorithm is introduced to assign a smaller weight to the path cost when the geodesic direction changes, which makes the region division more “cohesive”, thus greatly reducing the number of turns in the path and making it more robust. The final optimization algorithm ensures the following characteristics: complete coverage of the target area, wide applicability of multiple area shapes, reasonable distribution of exploration tasks, minimum average waiting time, and sustainable exploration without any preparation phase.

Currently, simultaneous localization and mapping (SLAM) is one of the main research topics in the robotics field. Visual-inertia SLAM, which consists of a camera and an inertial measurement unit (IMU), can significantly improve robustness and enable scale weak-visibility, whereas monocular visual SLAM is scale-invisible. For ground mobile robots, the introduction of a wheel speed sensor can solve the scale weak-visibility problem and improve robustness under abnormal conditions. In this paper, a multi-sensor fusion SLAM algorithm using monocular vision, inertia, and wheel speed measurements is proposed. The sensor measurements are combined in a tightly coupled manner, and a nonlinear optimization method is used to maximize the posterior probability to solve the optimal state estimation. Loop detection and back-end optimization are added to help reduce or even eliminate the cumulative error of the estimated poses, thus ensuring global consistency of the trajectory and map. The outstanding contribution of this paper is that the wheel odometer pre-integration algorithm, which combines the chassis speed and IMU angular speed, can avoid the repeated integration caused by linearization point changes during iterative optimization; state initialization based on the wheel odometer and IMU enables a quick and reliable calculation of the initial state values required by the state estimator in both stationary and moving states. Comparative experiments were conducted in room-scale scenes, building scale scenes, and visual loss scenarios. The results showed that the proposed algorithm is highly accurate—2.2 m of cumulative error after moving 812 m (0.28%, loopback optimization disabled)—robust, and has an effective localization capability even in the event of sensor loss, including visual loss. The accuracy and robustness of the proposed method are superior to those of monocular visual inertia SLAM and traditional wheel odometers.