Mobile robot autonomous path planning based on IBWO-TEB algorithm

To solve problems such as low planning efficiency, insufficient dynamics and poor environmental adaptability when mobile robots independently carry out path planning, a path planning method combining improved beluga whale optimization (IBWO) and timed elastic band (TEB) was proposed. Firstly, the IBWO algorithm was improved by a quasi-opposition learning mechanism and adaptive spiral predation strategy was used for global planning, to obtain the optimal path in the global optimization stage. Secondly, in the local planning stage, the improved two-stage TEB algorithm was used to locally adjust and optimize the global optimal path according to the current real-time environment. Finally, simulation and experiments were carried out in different scenarios, and the simulation results show that the IBWO-TEB algorithm was shorter than other algorithms in terms of driving distance and planning time. The experiment verified the excellent real performance of the IBWO-TEB algorithm, which can effectively help mobile robots to complete autonomous path planning.