Purpose: Autonomous obstacle avoidance is important in unmanned surface vehicle (USV) navigation. Although the result of obstacle detection is often inaccurate because of the inherent errors of LIDAR, conventional methods typically emphasize on a single obstacle-avoidance algorithm and neglect the limitation of sensors and safety in a local region. Conventional methods also fail in seamlessly integrating local and global obstacle avoidance algorithms. This paper aims to present a cooperative manoeuvring approach including both local and global obstacle avoidance. Design/methodology/approach: The global algorithm used in our USV is the Artificial Potential Field-Ant Colony Optimization (APF-ACO) obstacle-avoidance algorithm, which plans a relative optimal path on the specified electronic map before the cruise of USV. The local algorithm is a multi-layer obstacle-avoidance framework based on a single LIDAR to present an efficient solution to USV path planning in the case of sensor errors and collision risks. When obstacles are within a layer, the USV uses a corresponding obstacle-avoidance algorithm. Then the USV moves towards the global direction according to fuzzy rules in the fuzzy layer. Findings: The presented method offers a solution for obstacle avoidance in a complex environment. The USV follows the global trajectory planed by the APF-ACO algorithm. While, the USV can bypass current obstacle in the local region based on the multi-layer method effectively. This fact was validated by simulations and field trials. Originality/value: The method presented in this paper takes advantage of algorithm integration that remedies errors of obstacle detection. Simulation and experiments were also conducted for performance evaluation.

This letter studies the bipartite consensus control problem of networked Euler-Lagrange systems under directed network topology with the coexistence of positive and negative interaction weights. An adaptive bipartite consensus control law is proposed for solving bipartite consensus in networked Euler-Lagrange systems with uncertain parameters. By exploiting the specific structure of the Laplacian matrix associated with cooperation-competition networks, we introduce a novel coordinate transformation based on which the corresponding convergence analysis of closed-loop controlled systems is provided. Finally, simulations conducted on a network composed by seven two-link revolute joint manipulators are provided to illustrate the theoretical results.

Among the shortcomings of the A* algorithm, for example, there are many search nodes in path planning, and the calculation time is long. This article proposes a three-neighbor search A* algorithm combined with artificial potential fields to optimize the path planning problem of mobile robots. The algorithm integrates and improves the partial artificial potential field and the A* algorithm to address irregular obstacles in the forward direction. The artificial potential field guides the mobile robot to move forward quickly. The A* algorithm of the three-neighbor search method performs accurate obstacle avoidance. The current pose vector of the mobile robot is constructed during obstacle avoidance, the search range is narrowed to less than three neighbors, and repeated searches are avoided. In the matrix laboratory environment, grid maps with different obstacle ratios are compared with the A* algorithm. The experimental results show that the proposed improved algorithm avoids concave obstacle traps and shortens the path length, thus reducing the search time and the number of search nodes. The average path length is shortened by 5.58%, the path search time is shortened by 77.05%, and the number of path nodes is reduced by 88.85%. The experimental results fully show that the improved A* algorithm is effective and feasible and can provide optimal results.

This brief addresses the group-bipartite consensus problem of multi-agent systems with cooperative-competitive interactions. By combining the characteristics of group consensus and bipartite consensus, the concept of group-bipartite consensus is introduced to specify multiple bipartite consensus behavior. A distributed control protocol is then proposed for the topology graphs with acyclic partition and sign-balanced couples. The network topology studied in this brief eliminates the constraint that negative links can only exist between different groups, and thus the weights between agents in the same group can be either positive or negative. Some necessary and sufficient conditions for solving group-bipartite consensus problems are established by constructing a new form of the Laplacian matrix associated with the directed communication graphs. A simulation example is given to validate the theoretical results.