Soft Computing Techniques Applied to Adaptive Hybrid Navigation Methods for Tethered Robots in Dynamic Environments
Abstract
ABSTRACT Tethered robots face unique constraints: the physical tether can cause mission failure through snagging or excessive tension. Existing methods either require global maps (Tethered A*) or lack tether awareness (Bug algorithms). We present the first hybrid framework combining reactive obstacle avoidance with predictive tether management. Our system integrates (1) a tether‐aware cost function for 3–5 step snag prediction, (2) fuzzy logic for dynamic tension adaptation, and (3) genetic algorithms for tether routing optimization. Validation on a humanoid robot (ATLAS‐T) with instrumented sensors demonstrates 52% fewer snag incidents versus state‐of‐the‐art methods (Tethered A*, TSLAM), 94.7% success in tether‐critical scenarios, and 43% better odometry‐drift resilience. Real‐world experiments confirm practical viability.
Fields of Study & Subjects
Robotic Path Planning Algorithms
Control and Dynamics of Mobile Robots
Robotics and Sensor-Based Localization
Publication details
- Journal / source
- Journal of Field Robotics
- Publisher
- Wiley
- Volume
- 43
- Issue
- 6
- Pages
- 3752–3782
- Publication date
- 16 April 2026
- Language
- EN