Soft Computing Techniques Applied to Adaptive Hybrid Navigation Methods for Tethered Robots in Dynamic Environments

In this work, adaptive hybrid navigation methods for tethered robots in the real-world are investigated for navigation in changing environments. Traditional navigation systems rely on simulations, and therefore overlook complexity and unpredictability of the real application. We present a new way of overcoming these restrictions through a combination of state of the art sensor fusion techniques for reducing sensor noise and variability with soft computing techniques such as fuzzy logic, evolutionary algorithms and neural networks. Next, we propose real time adaptive path planning methods, addressing the problem of moving obstacles and varying tether configuration using optimal control for navigation efficiency. Combination of bug algorithms with soft computing frameworks improves resilience and responsiveness in uncertain contexts. We also suggest sophisticated retrieval methods for efficient tether management after navigation. Through substantial simulations and real world trials, we demonstrate the effectiveness of the proposed strategy, which would significantly boost navigation reliability and system adaptability in complex environments. Results show that our method provides improved tethered robot performance, which guarantees reliable operation in dynamic environments. Information & Authors Information Version history Peer review timeline Published Journal of Field Robotics Version of Record16 Apr 2026Published Copyright This work is licensed under a Non Exclusive No Reuse License.

Authors Metrics & Citations Metrics Article Usage 605views 212downloads Citations Download citation Chandan Sheikder, Weimin Zhang, Xiaopeng Chen, et al. Soft Computing Techniques Applied to Adaptive Hybrid Navigation Methods for Tethered Robots in Dynamic Environments. Authorea. 24 January 2025. DOI: https://doi.org/10.22541/au.173772926.68166096/v1 DOI: https://doi.org/10.22541/au.173772926.68166096/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu. Cited by - LiDAR-IMU Sensor Fusion-Based SLAM for Enhanced Autonomous Navigation in Orchards, Agriculture, 15, 17, (1899), (2025).https://doi.org/10.3390/agriculture15171899 Loading...