João Macedo
ABSTRACT
Locating odour sources with mobile robots is a difficult task with many applications. Over the years, researchers have devised bio-inspired and cognitive methods to enable mobile robots to fulfil this task. Cognitive approaches are effective in large spaces, but computationally heavy. On the other hand, bio-inspired ones are lightweight, but they are only effective in the presence of frequent stimuli. One of the most popular cognitive approaches is Infotaxis, which iteratively computes a probability map of the source location. Another strand of work uses Genetic Programming to produce complete search strategies from bio-inspired behaviours. This work combines the two approaches by allowing Genetic Programming to evolve search strategies that include infotactic and bio-inspired behaviours. The proposed method is tested in a set of environments with distinct airflow and chemical dispersion patterns. Its performance is compared to that of evolved strategies without infotactic behaviours and to the standard infotaxis approach. The statistically validated results show that the proposed method produces search strategies that have significantly higher success rates, whilst being faster than those produced by any of the original approaches. Moreover, the best evolved strategies are analysed, providing insight into when infotaxis is more beneficial.
- Xinxing Chen and Jian Huang. 2020. Combining particle filter algorithm with bio-inspired anemotaxis behavior: A smoke plume tracking method and its robotic experiment validation. Measurement 154 (2020), 107482.Google Scholar
Cross Ref
- Xu-Yang Dai, Jia-Ying Wang, and Qing-Hao Meng. 2019. An Infotaxis-based Odor Source Searching Strategy for a Mobile Robot Equipped with a TDLAS Gas Sensor. In 2019 Chinese Control Conference (CCC). IEEE, 4492--4497.Google ScholarCross Ref
- A. E. Eiben and J. E. Smith. 2003. Introduction to evolutionary computing. Vol. 53. Springer.Google Scholar
- Tao Jing, Qing-Hao Meng, and Hiroshi Ishida. 2021. Recent Progress and Trend of Robot Odor Source Localization. IEEJ Transactions on Electrical and Electronic Engineering (2021).Google Scholar
- João Macedo, Lino Marques, and Ernesto Costa. 2019. A Comparative Study of Bio-Inspired Odour Source Localisation Strategies from the State-Action Perspective. Sensors 19, 10 (2019), 2231.Google ScholarCross Ref
- João Macedo, Lino Marques, and Ernesto Costa. 2020. Locating Odour Sources with Geometric Syntactic Genetic Programming. In European Conference on the Applications of Evolutionary Computation. Springer.Google Scholar
- João Macedo, Lino Marques, and Ernesto Costa. 2021. Designing fitness functions for odour source localisation. In Proceedings of the Genetic and Evolutionary Computation Conference Companion. 103--104.Google ScholarDigital Library
- João Macedo, Lino Marques, and Ernesto Costa. 2021. Evolving Infotaxis for Meandering Environments. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 8431--8436.Google Scholar
- Eduardo Martin Moraud and Dominique Martinez. 2010. Effectiveness and robustness of robot infotaxis for searching in dilute conditions. Frontiers in neurorobotics 4 (2010), 1.Google Scholar
- Juan Duque Rodríguez, David Gómez-Ullate, and Carlos Mejía-Monasterio. 2017. On the performance of blind-infotaxis under inaccurate modeling of the environment. The European Physical Journal Special Topics 226, 10 (2017), 2407--2420.Google ScholarCross Ref
- Julian Ruddick, Ali Marjovi, Faezeh Rahbar, and Alcherio Martinoli. 2018. Design and performance evaluation of an infotaxis-based three-dimensional algorithm for odor source localization. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 1413--1420.Google ScholarDigital Library
- R. A. Russell, A. Bab-Hadiashar, R. L. Shepherd, and G. G. Wallace. 2003. A comparison of reactive robot Chemotaxis algorithms. Robotics and Autonomous Systems 45, 2 (2003), 83 -- 97.Google ScholarCross Ref
- Cheng Song, Yuyao He, Branko Ristic, and Xiaokang Lei. 2020. Collaborative infotaxis: Searching for a signal-emitting source based on particle filter and Gaussian fitting. Robotics and Autonomous Systems 125 (2020), 103414.Google ScholarDigital Library
- Massimo Vergassola, Emmanuel Villermaux, and Boris I Shraiman. 2007. 'Infotaxis' as a strategy for searching without gradients. Nature 445, 7126 (2007), 406--409.Google Scholar
Index Terms
- Hybridizing bio-inspired strategies with infotaxis through genetic programming
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