Scalable information-theoretic path planning for a rover-helicopter team in uncertain environments. (22nd April 2021)
- Record Type:
- Journal Article
- Title:
- Scalable information-theoretic path planning for a rover-helicopter team in uncertain environments. (22nd April 2021)
- Main Title:
- Scalable information-theoretic path planning for a rover-helicopter team in uncertain environments
- Authors:
- Folsom, Larkin
Ono, Masahiro
Otsu, Kyohei
Park, Hyoshin - 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 plannedMission-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. … (more)
- Is Part Of:
- International journal of advanced robotic systems. Volume 18:Number 2(2021)
- Journal:
- International journal of advanced robotic systems
- Issue:
- Volume 18:Number 2(2021)
- Issue Display:
- Volume 18, Issue 2 (2021)
- Year:
- 2021
- Volume:
- 18
- Issue:
- 2
- Issue Sort Value:
- 2021-0018-0002-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-04-22
- Subjects:
- Path planning and navigation -- mobile robots and multirobot systems -- multiagent robot teams -- sensor and information fusion -- robot sensors and sensor networks -- uncertainty reduction -- machine vision -- vision systems
Robotics -- Periodicals
Robotics
Periodicals
629.892 - Journal URLs:
- http://arx.sagepub.com/ ↗
http://search.epnet.com/direct.asp?db=bch&jid=13CR&scope=site ↗
http://www.intechweb.org/journal.php?id=3 ↗
http://www.uk.sagepub.com/home.nav ↗ - DOI:
- 10.1177/1729881421999587 ↗
- Languages:
- English
- ISSNs:
- 1729-8806
- Deposit Type:
- Legaldeposit
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