Robot Navigation in Dynamic Environments Using Soft Constrained Model Predictive Control with Control Barrier Function
A new model predictive control framework that integrates soft constraints based on control barrier function to enable robots to navigate efficiently and safely in dynamic environments.
Memory-Enhanced Deep Reinforcement Learning for Socially Aware Robot Navigation in Crowded Environments
A memory-enabled deep reinforcement learning framework is proposed to enhance autonomous robot navigation in diverse and crowded pedestrian environments by leveraging long-term memory, modeling human-robot interactions, and integrating global planning.
Legibot: Enhancing Robot Legibility through Cost-Based Local Motion Planning
A novel approach to incorporate legibility into local motion planning for mobile robots, enabling them to generate legible motions in real-time and dynamic environments.
Adaptive Multi-Objective Robot Navigation with Demonstration-Infused Reinforcement Learning
A multi-objective reinforcement learning framework that enables robots to adapt their navigation behavior to changing user preferences without retraining, by incorporating demonstration data as a tuneable objective.
Traversability-aware Adaptive Optimization for Robust Navigation in Extreme Mountainous Terrain
A traversability-aware navigation framework that integrates apparent traversability from exteroceptive sensors and relative traversability from proprioceptive sensors to generate an optimal path and adaptively control the robot's velocity for robust navigation in extreme mountainous terrain.
Enhancing Robot Navigation Efficiency Using Embedded Cellular Automata with Active Cells
This article presents an autonomous robot navigation system that leverages an embedded control navigation map utilizing cellular automata with active cells to effectively navigate in an environment containing various types of obstacles.
Integrating Predictive Motion Uncertainties with Distributionally Robust Risk-Aware Control for Safe Robot Navigation in Crowds
Sicherstellung der sicheren Navigation von Robotern in menschengefüllten Umgebungen durch die Integration von vorhersagbaren Bewegungsunsicherheiten in ein Verteilungsrobustes risikobewusstes Regelungssystem.
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