Alhassow, Mustafa MohammedAta, OğuzAtilla, Doğu Çağdaş2024-11-012024-11-012024Alhassow, M. M., Ata, O., Atilla, D. Ç. (2024). Obstacle avoidance capability for multi-target path planning in different styles of search. Computers, Materials and Continua, 81(1), 749-771. 10.32604/cmc.2024.0555921546-2218https://hdl.handle.net/20.500.12939/4956This study investigates robot path planning for multiple agents, focusing on the critical requirement that agents can pursue concurrent pathways without collisions. Each agent is assigned a task within the environment to reach a designated destination. When the map or goal changes unexpectedly, particularly in dynamic and unknown environments, it can lead to potential failures or performance degradation in various ways. Additionally, priority inheritance plays a significant role in path planning and can impact performance. This study proposes a Conflict- Based Search (CBS) approach, introducing a unique hierarchical searchmechanism for planning paths formultiple robots. The study aims to enhance flexibility in adapting to different environments. Three scenarioswere tested, and the accuracy of the proposed algorithm was validated. In the first scenario, path planning was applied in unknown environments, both stationary and mobile, yielding excellent results in terms of time to arrival and path length,with a time of 2.3 s. In the second scenario, the algorithmwas applied to complex environments containing sharp corners and unknown obstacles, resulting in a time of 2.6 s, with the algorithm also performing well in terms of path length. In the final scenario, themulti-objective algorithmwas tested in awarehouse environment containing fixed,mobile, andmulti-targeted obstacles, achieving a result of up to 100.4 s. Based on the results and comparisons with previous work, the proposed method was found to be highly effective, efficient, and suitable for various environments.eninfo:eu-repo/semantics/openAccessConflict algorithmDynamic environmentMobile robotOmnidirectional mobile robotUnknown environmentWarehouseArticle8117497712-s2.0-85206834921Q1WOS:001350270600016N/A