Results of the summarizing process for the arXiv paper: 2309.14272v1

, , , , Authors Reiya Takemura and Genya Ishigami present a study on perception-and-energy-aware motion planning for unmanned aerial vehicles (UAVs) operating in GNSS-denied environments. In these challenging conditions, UAVs must navigate efficiently and reliably without the aid of GNSS signals. The proposed planner addresses this by optimizing a cost function that considers both the total energy consumption of the UAV and the quality of perception provided by a Light Detection and Ranging (LiDAR) sensor mounted on the UAV. To enhance its effectiveness, a high-fidelity simulator is used to gather a flight dataset, which is then utilized to learn about energy consumption patterns and heteroscedastic uncertainty associated with LiDAR measurements. These models are specifically designed to account for variations in horizontal velocity, enabling the online planner to accurately estimate energy usage and perception quality. By doing so, the planner aims to reduce battery consumption while minimizing localization errors during navigation. Simulation experiments conducted in a photorealistic environment validate the efficacy of the proposed planner in balancing energy efficiency with perception quality under heteroscedastic uncertainty. The results demonstrate that by incorporating learning-based models and effectively addressing uncertainties, UAVs can navigate GNSS-denied environments more efficiently and reliably. This research was submitted for presentation at the 2024 IEEE International Conference on Robotics and Automation (ICRA), providing valuable insights into improving motion planning strategies for UAVs operating in challenging GNSS-denied conditions. Additionally, the authors have made their open-source code available for further exploration and development at https://gitlab.com/ReI08/perception-energy-planner.

• Study focuses on perception-and-energy-aware motion planning for UAVs in GNSS-denied environments
• Proposed planner optimizes a cost function considering energy consumption and LiDAR perception quality
• High-fidelity simulator used to gather flight dataset for learning energy consumption patterns and uncertainty in LiDAR measurements
• Models designed to account for variations in horizontal velocity for accurate estimation of energy usage and perception quality
• Planner aims to reduce battery consumption while minimizing localization errors during navigation
• Simulation experiments validate efficacy of planner in balancing energy efficiency with perception quality under uncertainty
• Research submitted for presentation at 2024 IEEE ICRA, offering insights into improving motion planning strategies for UAVs in GNSS-denied conditions
• Authors have shared open-source code for further exploration and development at https://gitlab.com/ReI08/perception-energy-planner

Introduction

Unmanned aerial vehicles (UAVs) have become increasingly popular in recent years due to their ability to perform a wide range of tasks, from surveillance and monitoring to package delivery. However, one major challenge that UAVs face is navigating in GNSS-denied environments where the signals from global navigation satellite systems (GNSS) are unavailable or unreliable. In such conditions, UAVs must rely on other sensors and technologies for navigation, making it crucial to optimize their energy consumption while maintaining reliable perception.

In this research paper, authors Reiya Takemura and Genya Ishigami propose a perception-and-energy-aware motion planning strategy for UAVs operating in GNSS-denied environments. The goal of this planner is to balance the trade-off between energy efficiency and perception quality by considering both factors in its cost function.

Motion Planning Strategy

The proposed planner utilizes a high-fidelity simulator to gather a flight dataset that includes information about energy consumption patterns and heteroscedastic uncertainty associated with LiDAR measurements. This dataset is then used to train learning-based models specifically designed for estimating energy usage and perception quality under varying horizontal velocities.

The online planner uses these trained models during flight operations to accurately estimate the energy consumption of the UAV as well as the quality of perception provided by its LiDAR sensor. By incorporating these estimates into its cost function, the planner can make decisions that minimize battery usage while also ensuring reliable localization during navigation.

Addressing Uncertainties

One key aspect of this research is addressing uncertainties associated with LiDAR measurements. Traditional motion planning strategies often assume homoscedastic uncertainty, which means that measurement errors remain constant regardless of velocity changes. However, real-world scenarios often involve heteroscedastic uncertainty where measurement errors vary depending on velocity changes.

To account for this type of uncertainty, the proposed planner uses a heteroscedastic model that can accurately estimate LiDAR measurement errors under varying horizontal velocities. This allows the planner to make more informed decisions and improve its overall performance in GNSS-denied environments.

Simulation Results

To validate the effectiveness of their proposed motion planning strategy, Takemura and Ishigami conducted simulation experiments in a photorealistic environment. The results showed that their planner was able to successfully balance energy efficiency with perception quality, even under heteroscedastic uncertainty.

Compared to traditional planners, the proposed strategy achieved significant improvements in both energy consumption and localization accuracy. These results demonstrate the potential of incorporating learning-based models and effectively addressing uncertainties in improving UAV navigation in challenging GNSS-denied environments.

Conclusion

In conclusion, this research paper presents a perception-and-energy-aware motion planning strategy for UAVs operating in GNSS-denied environments. By considering both energy consumption and perception quality in its cost function and addressing uncertainties through learning-based models, this planner offers an effective solution for navigating UAVs efficiently and reliably without relying on GNSS signals.

The authors' use of a high-fidelity simulator to gather flight data and their open-source code available for further exploration make this research valuable for future developments in motion planning strategies for UAVs. With continued advancements in technology, it is crucial to have efficient and reliable navigation solutions for UAVs operating in challenging conditions, making this study highly relevant and impactful.