The field of autonomous robotics is moving towards increased automation and adaptability in environmental monitoring and motion planning. Recent developments have focused on integrating robotics with environmental science to improve data collection and monitoring in challenging terrains. Autonomous robots are being used to navigate complex environments, such as wetlands and alpine scree habitats, to collect data on greenhouse gas emissions and plant species. Advances in motion planning have also enabled robots to adapt to dynamic environments and navigate through obstacles with increased efficiency. Notable papers in this area include:

• WetExplorer, which presents an autonomous mobile robot for automating wetland greenhouse-gas surveys.
• SBAMP, which introduces a novel framework for sampling-based adaptive motion planning.
• RRT*former, which proposes a novel sampling-based planning algorithm that integrates a Transformer network to extract features from the environment and leverage information from previous samples.