Multirobot Coverage Search in Three Dimensions

Exploration is an important aspect of robotics, whether it is for mapping, rescue missions or path planning in an unknown environment. Frontier Exploration planning (FEP) and Receding Horizon Next-Best-View planning (RH-NBVP) are two different approaches with different strengths and weaknesses. FEP explores a large environment consisting of separate regions with ease, but is slow at reaching full exploration due to moving back and forth between regions. RH-NBVP shows great potential and efficiently explores individual regions, but has the disadvantage that it can get stuck in large environments not exploring all regions. In this work we present a method that combines both approaches, with FEP as a global exploration planner and RH-NBVP for local exploration. We also present techniques to estimate potential information gain faster, to cache previously estimated gains and to exploit these to efficiently estimate new queries.

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“…Exploration using multiple robots has been done in several works, e.g. [13], [14], to reduce the total exploration time.…”

Section: Related Workmentioning

confidence: 99%

Efficient Autonomous Exploration Planning of Large-Scale 3-D Environments

Selin¹,

Tiger²,

Duberg³

et al. 2019

IEEE Robot. Autom. Lett.

176

201

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“…Approaches to solving the traveling salesman problem (TSP) have been investigated over the years ranging from the use of genetic algorithms in an iterative approach [13] to the optimization of multiple simultaneous TSPs [14]. The work in [15] discusses the use of multiple robots to cover a 3D searching area, which is formulated as a single TSP. The use of greedy and optimal solutions in [15] directed the four robots to efficiently cover areas for a search-and-rescue type scenario.…”

Section: 2: Research Significancementioning

confidence: 99%

“…The work in [15] discusses the use of multiple robots to cover a 3D searching area, which is formulated as a single TSP. The use of greedy and optimal solutions in [15] directed the four robots to efficiently cover areas for a search-and-rescue type scenario. While extending the TSP to three dimensions, the robots in [15] were restricted to one form of locomotion, which indicates obstacles are treated as traversable only if a viable path, such as a ramp, was available.…”

Section: 2: Research Significancementioning

confidence: 99%

Motion Planning and Task Allocation for a Jumping Rover Team

Tan

Jung

Shyu

et al. 2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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The next frontier of interplanetary exploration missions would encounter countless unpredictable geographical challenges including uninhabitable caves, icy craters of the Moon and Mars, unsustainable mountain cliffs, high radiation areas, and extreme temperature environments. This research will design a fully autonomous and cooperative robotics team composed of unmanned ground vehicles (UGVs) with hybrid operational modes to tackle the multiple traveling salesman problem (mTSP) and to overcome environmental obstacles, to accomplish the challenging interplanetary exploration missions. The hybrid operational modes allow every UGV Firstly, I would like to express my sincere gratitude to my advisor, Dr. Ran Dai for the continuous support of my Honors Undergraduate Research Program, for her patience, motivation, and immense knowledge of robotics, automation, and optimization. Her guidance helped me in all the time of research and writing of this thesis. I could not have imaged having a better advisor and mentor for my Honors Undergraduate Research Program. Because of Dr. Dai, I managed to have my first conference paper submitted to the 2020 International Conference on Robotics and Automation (ICRA) as a first author. Without her strong support, it would not be possible to conduct this research. My sincere thanks also go to Dr. Carlos Castro, who provided me insightful comments and encouragement on being an outstanding presenter as a Buckeye engineering student to prepare me for my Oral Defense. Furthermore, Dr. Castro always willing to listen patiently and to provide advice when I faced unpredictable challenges on my research like a closed labmate of mine quitted the research team out of a sudden. Besides my advisor and Dr. Castro, I would like to thank Raymond Brooks, who is my laboratory supervisor at Department of Engineering Education, and Ali Rahimiardestani, who is my lab partner for giving me an access to use laser-cutting machine and 3D-printers to manufacture my research jumping rovers for free, teaching me the proper methods of operating the laser-cutting machine and 3D-printers and teaching me efficient techniques of soldering my rovers' electronic components. Without my lab supervisor and lab partner guidance and support, it would take me more time and cost me more money to build a team of jumping rovers. iv I thank my fellow Automation and Optimization lab mates, MyungJin Jung, Changhuang Wan, and Isaac Shyu in for the stimulating discussion, for the sleepless night we were working together before deadlines, and for all the fun we had in the last four semesters. Because of their advice, suggestions, encouragement, and constructive criticism, I learned several ingenious heuristic techniques to solve and optimize a path planning problem with MATLAB and run an indoor simulation efficiently with robots and the VICON system used to track the position of objects. Last but not least, I would like to thank my family: my parents, grandparents, my only younger brother and my relatives for supporting me spiritually ...

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“…Our primary motivation is object classification tasks, however a variety of other tasks can be formulated in a similar way. Coverage tasks are most similar to our problem, which require a team of robots to collectively observe every location in an environment (Galceran and Carreras, 2013;Dornhege et al, 2016;Hönig and Ayanian, 2016;Bircher et al, 2016). Target tracking and search problems require using the sensing capabilities of multiple robots to locate and maintain contact with targets (Robin and Lacroix, 2015;Xu et al, 2013;Charrow, 2015).…”

Section: Related Workmentioning

confidence: 99%

Online planning for multi-robot active perception with self-organising maps

2017

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We propose a self-organising map (SOM) algorithm as a solution to a new multi-goal path planning problem for active perception and data collection tasks. We optimise paths for a multi-robot team that aims to maximally observe a set of nodes in the environment. The selected nodes are observed by visiting associated viewpoint regions defined by a sensor model. The key problem characteristics are that the viewpoint regions are overlapping polygonal continuous regions, each node has an observation reward, and the robots are constrained by travel budgets. The SOM algorithm jointly selects and allocates nodes to the robots and finds favourable sequences of sensing locations. The algorithm has a runtime complexity that is polynomial in the number of nodes to be observed and the magnitude of the relative weighting of rewards. We show empirically the runtime is sublinear in the number of

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Multirobot Coverage Search in Three Dimensions

Cited by 23 publications

References 37 publications

Efficient Autonomous Exploration Planning of Large-Scale 3-D Environments

Efficient Autonomous Exploration Planning of Large-Scale 3-D Environments

Motion Planning and Task Allocation for a Jumping Rover Team

Online planning for multi-robot active perception with self-organising maps

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