Abstract

Mission-critical exploration of uncertain environments requires reliable and robust mechanisms for achieving information gain. Typical measures of information gain such as Shannon entropy and KL divergence are unable to distinguish between different bimodal probability distributions or introduce bias toward one mode of a bimodal probability distribution. The use of a standard deviation (SD) metric reduces bias while retaining the ability to distinguish between higher and lower risk distributions. Areas of high SD can be safely explored through observation with an autonomous Mars Helicopter allowing safer and faster path plans for ground-based rovers. First, this study presents a single-agent information-theoretic utility-based path planning method for a highly correlated uncertain environment. Then, an information-theoretic two-stage multiagent rapidly exploring random tree framework is presented, which guides Mars helicopter through regions of high SD to reduce uncertainty for the rover. In a Monte Carlo simulation, we compare our information-theoretic framework with a rover-only approach and a naive approach, in which the helicopter scouts ahead of the rover along its planned path. Finally, the model is demonstrated in a case study on the Jezero region of Mars. Results show that the information-theoretic helicopter improves the travel time for the rover on average when compared with the rover alone or with the helicopter scouting ahead along the rover’s initially planned route.

Highlights

  • In highly uncertain environments, maximizing information gain is vital to ensuring safe, efficient, autonomous exploration

  • Our research focuses on regions containing 40,000 to 673,854 1 m2 cells allowing for multiple days (Martian Sol) of rover travel time and is beyond the scope of traditional POMDP approaches

  • This research offers a set of novel techniques for single and multiagent path planning in uncertain environments

Read more

Summary

Introduction

In highly uncertain environments, maximizing information gain is vital to ensuring safe, efficient, autonomous exploration. Words, the most important information is found in the regions in which true speed of the rover could deviate significantly from the expected value Efficient exploration of these regions with an unmanned information-theoretic helicopter reduces the rover’s travel time uncertainty and enables the rover to adjust its planned route if the conditions inside the highly uncertain region are found to be more optimal. A rapidly exploring random tree (RRT)-based two-stage multiagent path-planning algorithm is presented, which computes travel time optimized and safe routes for a Mars rover to successfully navigate through rugged, uncertain terrain with the aid of a cooperating information-theoretic helicopter. Path planning using the worst-case travel time for each grid cell in the map can provide maximum safety but results in an overly cautious route, which is excessively slow in many cases and inappropriate for missions with time restrictions or deployments in which saved time allows the rover to perform additional nondriving-related scientific tasks

Related work
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call