Reactive planning for olfactory-based mobile robots

Pang, Shuo; Zhu, Fengsen

doi:10.1109/iros.2009.5353993

Cited by 12 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Algorithms for constructing such maps include Bayesian inference, particle filters, stochastic mapping [41], source term estimation [42], information-based search [43], partially observable Markov decision processes [44], combination of infotaxis and Dijkstra algorithm [45], etc. Subsequently, robots are guided towards the estimated source via path-planning algorithms such as artificial potential fields, A-star [46,47]. These models also rely on olfaction sensing for estimating the odor source.…”

Section: Related Workmentioning

confidence: 99%

Robotic Odor Source Localization via Vision and Olfaction Fusion Navigation Algorithm

Hassan,

Wang,

Mahmud

2024

Sensors

View full text Add to dashboard Cite

Robotic odor source localization (OSL) is a technology that enables mobile robots or autonomous vehicles to find an odor source in unknown environments. An effective navigation algorithm that guides the robot to approach the odor source is the key to successfully locating the odor source. While traditional OSL approaches primarily utilize an olfaction-only strategy, guiding robots to find the odor source by tracing emitted odor plumes, our work introduces a fusion navigation algorithm that combines both vision and olfaction-based techniques. This hybrid approach addresses challenges such as turbulent airflow, which disrupts olfaction sensing, and physical obstacles inside the search area, which may impede vision detection. In this work, we propose a hierarchical control mechanism that dynamically shifts the robot’s search behavior among four strategies: crosswind maneuver, Obstacle-Avoid Navigation, Vision-Based Navigation, and Olfaction-Based Navigation. Our methodology includes a custom-trained deep-learning model for visual target detection and a moth-inspired algorithm for Olfaction-Based Navigation. To assess the effectiveness of our approach, we implemented the proposed algorithm on a mobile robot in a search environment with obstacles. Experimental results demonstrate that our Vision and Olfaction Fusion algorithm significantly outperforms vision-only and olfaction-only methods, reducing average search time by 54% and 30%, respectively.

show abstract

Section: Related Workmentioning

confidence: 99%

Robotic Odor Source Localization via Vision and Olfaction Fusion Navigation Algorithm

Hassan,

Wang,

Mahmud

2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Algorithms for constructing such maps include Bayesian inference, particle filters , stochastic mapping [32], source term estimation [33], information-based search [34], partially observable Markov decision processes [35], etc. Subsequently, robots are guided towards the estimated source via path planning algorithms such as artificial potential fields, A-star [36,37]. These models also rely on olfaction sensing for estimating the odor source.…”

Section: Related Workmentioning

confidence: 99%

Robotic Odor Source Localization via Vision and Olfaction Fusion Navigation Algorithm

Hassan,

Wang,

Mahmud

2024

Preprint

View full text Add to dashboard Cite

Robotic odor source localization (OSL) is a technology that enables mobile robots or autonomous vehicles to find an odor source in unknown environments. An effective navigation algorithm that guides the robot to approach the odor source is the key to successfully locating the odor source. While traditional OSL approaches primarily utilize an Olfaction-only strategy, guiding robots to find the odor source by tracing emitted odor plumes, our work introduces a fusion navigation algorithm that combines both vision and olfaction-based techniques. This hybrid approach addresses challenges such as turbulent airflow, which disrupts olfaction sensing, and physical obstacles inside the search area, which may impede vision detection. In this work, we propose a hierarchical control mechanism that dynamically shifts the robot’s search behavior among four strategies: crosswind maneuver, Obstacle-avoid Navigation, Vision-based Navigation, and Olfaction-based Navigation. Our methodology includes a custom-trained deep-learning model for visual target detection and a moth-inspired algorithm for Olfaction-based navigation. To assess the effectiveness of our approach, we implemented the proposed algorithm on a mobile robot in a search environment with obstacles. Experimental results demonstrate that our Vision and Olfaction Fusion algorithm significantly outperforms Vision-only and Olfaction-only methods, reducing average search time by 54% and 30%, respectively.

show abstract

“…Specific details about the APF olfactory path-planning method are available in the literature. 22 The main works of this article were as follows: Firstly, the POMDP method is used to estimate the probability distribution of the source of the chemical plume, and the source likelihood map is obtained. Secondly, according to the characteristics of the probability distribution map, the chemical plume is tracked by the APF method.…”

Section: Introductionmentioning

confidence: 99%

Underwater chemical plume tracing based on partially observable Markov decision process

Hai-Feng

Chen

Deng

et al. 2019

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

Chemical plume tracing based on autonomous underwater vehicle uses chemical as a guidance to navigate and search in the unknown environments. To solve the key issue of tracing and locating the source, this article proposes a path-planning strategy based on partially observable Markov decision process algorithm and artificial potential field algorithm. The partially observable Markov decision process algorithm is used to construct a source likelihood map and update it in real time with environmental information from the sensors on autonomous underwater vehicle in search area. The artificial potential field algorithm uses the source likelihood map for accurately planning tracing path and guiding the autonomous underwater vehicle to track along the path until the source is detected. This article carries out simulation experiments on the proposed algorithm. The experimental results show that the algorithms have good performance, which is suitable for chemical plume tracing via autonomous underwater vehicle. Compared with the bionic method, the simulation results show that the proposed method has higher success rate and better stability than the bionic method.

show abstract

Reactive planning for olfactory-based mobile robots

Cited by 12 publications

References 29 publications

Robotic Odor Source Localization via Vision and Olfaction Fusion Navigation Algorithm

Robotic Odor Source Localization via Vision and Olfaction Fusion Navigation Algorithm

Robotic Odor Source Localization via Vision and Olfaction Fusion Navigation Algorithm

Underwater chemical plume tracing based on partially observable Markov decision process

Contact Info

Product

Resources

About