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for heterogeneous multi-vehicle systems, International Journal of Applied Mathematics and Computer Science 26(2): 297-308, DOI: 10.1515/amcs-2016-0021. Latombe, J.-C. (2012). Robot Motion Planning, Springer, New York, NY. LaValle, S.M. (1998). Rapidly-Exploring Random Trees: A New Tool for Path Planning, Iowa State University, Ames, IA. Liu, J., Yang, J., Liu, H., Tian, X. and Gao, M. (2017). An improved ant colony algorithm for robot path planning, Soft Computing 21(19): 5829-5839. Lozano-Pérez, T. and Wesley, M.A. (1979). An algorithm for planning collision-free

-P’erez, T. and Wesley, M., 2000. An algorithm for planning collision-free paths among polyhedral obstacles. Communication of the ACM, 22, pp.560-570. Murphy, R., 2000. Introduction to AI robotics. Massachusetts: MIT Press. Manley, J.E., 2008. Unmanned surface vehicles, 15 years of development. In Proceedings of MTS/IEEE Oceans'08, Quebec City, Canada, 15 - 18 September 2008, pp.1-4. Naeem, W., Irwin, G.W. and Yang, A., 2011. COLREGs-based collision avoidance strategies for unmanned surface vehicle. Mechatronics, 22, pp.669-678. Patel, A., 2000. Amit’s Game Programming


To achieve the overall goal of realising an efficient and advantageous participation of autonomous ground vehicles in the transport system as fast as possible, a lot of work is being done in different and specific research fields. One of the most important research fields, which has a large impact on safe autonomous ground vehicle realisation, is the development of path planning algorithms. Therefore, this work describes in detail the development and application of a hybrid path planning algorithm. The described algorithm is based on classical and heuristic path planning approaches and can be applied in unstructured and structured environments. The efficiency of the algorithm was investigated by applying the algorithm and executing theoretical and experimental tests. The theoretical and experimental tests were executed while optimising different complexity paths. Results analysis demonstrated that the described algorithm can generate a smooth, dynamically feasible and collision-free path.


In recent years, unmanned surface vehicles have been widely used in various applications from military to civil domains. Seaports are crowded and ship accidents have increased. Thus, collision accidents occur frequently mainly due to human errors even though international regulations for preventing collisions at seas (COLREGs) have been established. In this paper, we propose a real-time obstacle avoidance algorithm for multiple autonomous surface vehicles based on constrained convex optimization. The proposed method is simple and fast in its implementation, and the solution converges to the optimal decision. The algorithm is combined with the PD-feedback linearization controller to track the generated path and to reach the target safely. Forces and azimuth angles are efficiently distributed using a control allocation technique. To show the effectiveness of the proposed collision-free path-planning algorithm, numerical simulations are performed.