This thesis investigates motion planning and control for snake-like robots operating in sparse, cluttered, and contact-rich environments. Departing from the conventional paradigm that treats obstacles solely as constraints, the work advances the concept of Obstacle-Aided Locomotion (OAL): deliberately regulating body–environment contacts to improve propulsion efficiency, stability, and robustness.

Contributions are threefold. First, the thesis situates OAL within a rigorous theoretical and algorithmic framework, reviewing classical and modern planning methods and identifying the specific challenges posed by elongated, articulated bodies. Second, it develops a validated digital twin and Simulation-in-the-loop (SIL) framework (MATLAB and CoppeliaSim) that provides high-fidelity contact measurements and enables data-driven gait optimization. Using PSO to tune serpentine gait parameters, the SIL optimizer balances forward progress against contact forces to discover energy-efficient and wear-minimal trade-off. Third, the thesis proposes a hierarchical planner that couples a bounded Generalized Voronoi Diagram with a local, contact-aware optimizer that exploits nearby obstacles when beneficial.
   
    Experimental validation in heterogeneous simulated scenes demonstrates that controlled obstacle interactions improve performance. Moreover, bounded Voronoi skeletons can be used as an effective global backbones for exploitation of nearby contact opportunities.
   
    The thesis concludes with a discussion of limitations and outlines of future research directions.