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A mobile robot is an autonomous agent, capable of planning the path from source to destination in both, a known, or an unknown environment. In this paper, we presented a novel approach to find the optimal trajectory. We have hybridized oppositional-based learning (OBL) with the evolutionary invasive weed optimization (IWO) technique to generate the optimal path for different robot(s). The navigation algorithm considered in this work is intelligent enough to fulfill the objective of minimizing the path length and the time to reach its specified goal. Oppositional IWO (OIWO) algorithm mimics the colonizing behavior of weed and uses the concept of quasi-opposite number, the concept of OBL is to find the shortest path between source and destination of mobile robot(s). An objective function has been formulated that takes care of the optimal target-seeking behavior as well as the obstacle avoidance of the mobile robot. In this technique, OBL considers the current population generated by IWO algorithm and its opposite population simultaneously to get better and faster convergence. The simulation and experimental results prove and validate the effectiveness of developed OIWO path planning algorithm.

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