A novel hybrid framework for single and multi-robot path planning in a complex industrial environment

Optimum path planning is a fundamental necessity for the successful functioning of a mobile robot in industrial applications. This research work investigates the application of the artificial bee colony (ABC) approach, probabilistic roadmap (PRM) method, and evolutionary programming (EP) algorithm to tackle the issue of single and multi-robot path planning in partially known or unknown industrial complex environments. Conventional techniques depend on external factors such as delay of information from one bee's stage to another for selecting neighbour food points. Due to this, its efficiency is comparatively low and might result in longer runtimes. To address these challenges, a novel hybrid framework based on ABC-PRM-EP has been introduced. Firstly, a suboptimal initial feasible path is attained by a new framework (ABC-PRM) within the mobile robot sensor detection range. Then, EP performs refinement of that attained suboptimal path to provide a short and optimum path. Also, a multi-robot collaboration strategy has been introduced based on the concept of hold-up. A number of comparative studies have been conducted in three different test scenarios with different complexity to validate the proposed framework efficiency and performance. Different performance indices such as path length (m), smoothness (rad), collision safety value, success rate, processing time (s), and convergence speed have been measured to validate the effectiveness of the proposed framework. The comparative analysis obtained from these test scenarios indicates that the proposed framework outperforms conventional ABC, ABC-EP and HPSO-GWO-EA, while performing path planning.