Adaptive Path Planning

Adaptive path planning focuses on creating robot navigation strategies that dynamically adjust to changing environments and objectives, optimizing for factors like efficiency, safety, and human interaction. Current research emphasizes integrating machine learning models, such as generative adversarial networks (GANs), convolutional neural networks (CNNs), and reinforcement learning (RL) algorithms, with classical planning methods like RRT* and Dijkstra's algorithm, to achieve robust and adaptable path generation in diverse scenarios, including human-robot interaction and off-road autonomous driving. This field is crucial for advancing robotics in various applications, from assistive care and data acquisition to autonomous vehicles and aerial surveillance, by enabling more efficient, safe, and human-centered robot navigation.