Gazebo

The 'Gazebo' dataset/benchmark is a simulation environment used to evaluate the effectiveness and versatility of control frameworks for legged robots, specifically in terms of stability and robustness against uncertainties and disturbances.

Papers using Gazebo (11)

A Whole-Body Disturbance Rejection Control Framework for Dynamic Motions in Legged Robots2025 · 2 citesTrajectory Planning with Model Predictive Control for Obstacle Avoidance Considering Prediction Uncertainty2025 · 1 citesFast-Revisit Coverage Path Planning for Autonomous Mobile Patrol Robots Using Long-Range Sensor Information2025 · 1 citesDRL-Based Pose Control for Double-Ackermann Robots Under Actuation Uncertainties2026Fast-SegSim: Real-Time Open-Vocabulary Segmentation for Robotics in Simulation2026From Data to Safe Mobile Robot Navigation: An Efficient and Modular Robust MPC Design Pipeline2025A Three-Level Whole-Body Disturbance Rejection Control Framework for Dynamic Motions in Legged Robots2025Refining Motion for Peak Performance: Identifying Optimal Gait Parameters for Energy-Efficient Quadrupedal Bounding2025Decentralized Uncertainty-Aware Multi-Agent Collision Avoidance with Model Predictive Path Integral2025EDEN: Entorhinal Driven Egocentric Navigation Toward Robotic Deployment2025Trajectory Adaptation using Large Language Models2025