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<p>Efficient autonomous robot navigation in unstructured and unknown terrains is critical for applications such as agriculture, disaster response, and exploration. Traditional path planning methods, such as Particle Swarm Optimisation (PSO) and Grey Wolf Optimisation (GWO), face challenges in dynamic environments, as they handle terrain classification and path planning as independent processes, lacking a unified framework that adapts navigation decisions based on real-time terrain feedback. This study presents a novel hybrid optimisation framework combining PSO, GWO, and Evolutionary Algorithms (EA) through a coordinated Exploration-Integration Hybrid Optimisation (EI-HO) pipeline that distinguishes it from conventional standalone methods, while using a Multi-Layer Perceptron (MLP) for accurate terrain classification based on multimodal sensor data, including Inertial Measurement Unit (IMU), vibration signals, and suspension parameters. The proposed system was evaluated using a comprehensive terrain dataset and tested in simulation environments like Robot Operating System (ROS) and Gazebo. The results showed that the proposed method achieved an accuracy of 0.92, a precision of 0.91, a recall of 0.90, and an F1-score of 0.91, outperforming PSO (accuracy = 0.85) and GWO (accuracy = 0.80). The Exploration-Integration Hybrid Optimisation achieved a balanced final objective score of 42.39, compared to 35.6 for Grey Wolf Optimisation and 54.7 for Particle Swarm Optimisation, confirming both navigation accuracy and convergence efficiency gains. Future work will focus on improving real-time learning and optimising computational efficiency.</p>

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Hybrid optimisation and heuristic algorithms for efficient robot navigation in unknown terrains

  • 期刊论文

  • 发表日期：

    2026年7月21日

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  • Donglin Wang

摘要

Efficient autonomous robot navigation in unstructured and unknown terrains is critical for applications such as agriculture, disaster response, and exploration. Traditional path planning methods, such as Particle Swarm Optimisation (PSO) and Grey Wolf Optimisation (GWO), face challenges in dynamic environments, as they handle terrain classification and path planning as independent processes, lacking a unified framework that adapts navigation decisions based on real-time terrain feedback. This study presents a novel hybrid optimisation framework combining PSO, GWO, and Evolutionary Algorithms (EA) through a coordinated Exploration-Integration Hybrid Optimisation (EI-HO) pipeline that distinguishes it from conventional standalone methods, while using a Multi-Layer Perceptron (MLP) for accurate terrain classification based on multimodal sensor data, including Inertial Measurement Unit (IMU), vibration signals, and suspension parameters. The proposed system was evaluated using a comprehensive terrain dataset and tested in simulation environments like Robot Operating System (ROS) and Gazebo. The results showed that the proposed method achieved an accuracy of 0.92, a precision of 0.91, a recall of 0.90, and an F1-score of 0.91, outperforming PSO (accuracy = 0.85) and GWO (accuracy = 0.80). The Exploration-Integration Hybrid Optimisation achieved a balanced final objective score of 42.39, compared to 35.6 for Grey Wolf Optimisation and 54.7 for Particle Swarm Optimisation, confirming both navigation accuracy and convergence efficiency gains. Future work will focus on improving real-time learning and optimising computational efficiency.