Simulation-based evaluation of a modified RRT algorithm for ground-robot navigation in 3D environments

Autonomous robot navigation requires path-planning algorithms that maintain reliable performance in environments involving dynamic obstacles, changing goals, and complex terrain. This study extends the evaluation of a previously proposed Modified Rapidly-Exploring Random Tree (Modified RRT) algorithm from two-dimensional benchmark maps to robotic navigation scenarios in three-dimensional simulated environments. A simulation-based robotic navigation framework was developed using ROS 2 and Gazebo. Five scenarios were investigated: reconstruction of selected benchmark maps in 3D simulation, dynamic obstacle avoidance, dynamic goal adaptation, warehouse/campus navigation, and slope traversal. The framework incorporates synchronized RGB and depth sensing with point-cloud generation to characterize simulated environmental observations and support the analysis of perception and navigation performance. The global path planner operates in the horizontal x-y plane, while the robot, obstacles, and terrain are represented in the three-dimensional simulation environment. All four planners were evaluated under a common protocol using a shared costmap, collision checker, stopping budget, and parameter-tuning procedure. Modified RRT achieved success rates of up to 98%, reduced navigation completion time by up to 17.6%, and generated paths 10–15% shorter than standard RRT and 2–10% shorter than the next-best planner, Informed RRT* (IRRT*), at the cost of higher planning time than standard RRT in most scenarios. Under ideal sensing, Modified RRT reduced the near-collision rate by up to 74% in structured environments and 63% in unstructured environments relative to standard RRT. When depth noise, missing measurements, latency, and registration errors were introduced, the reductions were 45–61% and 40–52%, respectively. The results indicate that the Modified RRT sampling mechanisms can improve path-planning and navigation performance across the evaluated simulation scenarios. The findings also show that perception imperfections reduce the near-collision-rate improvements observed under ideal sensing. Overall, this study presents a simulation-based framework for evaluating planar global path planning, robot navigation execution, and simulated multimodal perception in three-dimensional robotic environments.