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This study aims to modify a nature-based numerical method named the invasive weed optimization (IWO) method for mobile robot path planning in various complex environments.

The existing IWO method is quick in converging to a feasible solution but in a complex environment; it takes more time as well as computational resources. So, in this paper, the computational part of this artificial intelligence technique is modified with the help of recently developed evolution algorithms like particle swarm optimization, genetic algorithm, etc. Some conditional logic statements were used while doing sensor-based mapping for exploring complex paths. Implementation of sensor-based exploration, mathematical IWO method and prioritizing them for better efficiency made this modified IWO method take complex dynamic decisions.

The proposed modified IWO is better for dynamic obstacle avoidance and navigating a long complex map. The deviation of results in simulation and experiments is less than 5.5%, which validates a good agreement between simulation and real-time testing platforms.

As per a deep literature review, it has found that the proposed approach has not been implemented on the Khepera-III robot for smooth motion planning. Here a dynamic obstacle mapping feature is implemented. A method to selectively distribute seeds instead of a random normal distribution is also implemented in this work. The modified version of IWO is coded in MATLAB and simulated through V-Rep simulation software. The integration of sensors was done through logical conditioning. The simulation results are validated using real-time experiments.