Multi-robot exploration and terrain coverage in an unknown environment

Senthilkumar, K.S.; Bharadwaj, Kamal Kant

doi:10.1016/j.robot.2011.09.005

Cited by 58 publications

(13 citation statements)

References 16 publications

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“…Therefore, the MRCPP methods using the grid map do not require a complicated map integration process as compared to the MRCPP methods using boustrophedon decomposition. Hazon et al [25] and Senthilkumar and Bharadwaj [26] proposed an MRCPP method based on the spanning tree coverage (STC) method [31] using a grid map. The size of the grid map was set to twice the cleaning robot's diameter.…”

Section: Related Workmentioning

confidence: 99%

Multi-Cleaning Robots Using Cleaning Distribution Method Based on Map Decomposition in Large Environments

Lee

Kang

2020

IEEE Access

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Most cleaning robots have a good cleaning performance for small environments such as houses but require a longer cleaning time due to problems such as slow cleaning progress and low battery capacity, making the robots unsuitable for large environments such as libraries and airports. Cleaning large environments with multiple robots is faster than cleaning them with a single robot. Multi-cleaning robots can utilize several robots to simultaneously clean and share the task of cleaning among the robots. However, as the number of robots increases and the effective distribution of cleaning is not efficient during the cleaning process, the cleaning time will consequently be longer due to the frequent collisions between the robots. Therefore, to shorten the cleaning time, multi-cleaning robots require a coverage path planning that uses an effective cleaning distribution method in the cleaning process. In this paper, a coverage path planning using a cleaning distribution method based on map decomposition is proposed to reduce the cleaning time of multi-cleaning robots. The experimental results demonstrated that the proposed multi-cleaning robots' coverage path planning could be used in large environments in the presence of several types of obstacles. Furthermore, the cleaning time was found to be shorter than that of the previous methods in the case of multi-cleaning robots. INDEX TERMS Coverage path planning, cleaning robot, computational efficiency, multi-robots system, robot path planning.

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Section: Related Workmentioning

confidence: 99%

Multi-Cleaning Robots Using Cleaning Distribution Method Based on Map Decomposition in Large Environments

Lee

Kang

2020

IEEE Access

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“…Each robot searches then in its assigned area following regular patterns. Another example can be found in [19], where the coverage task is addressed using the spanning tree coverage method. In any case, the strategies proposed in these works do not differ from the strategies listed above.…”

Section: Search With Swarmsmentioning

confidence: 99%

Comparison of Heuristic Algorithms in Discrete Search and Surveillance Tasks Using Aerial Swarms

Garcia-Aunon

Barrientos

2018

Applied Sciences

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Abstract:The search of a given area is one of the most studied tasks in swarm robotics. Different heuristic methods have been studied in the past taking into account the peculiarities of these systems (number of robots, limited communications and sensing and computational capacities). In this work, we introduce a behavioral network made up of different well-known behaviors that act together to achieve a good performance, while adapting to different scenarios. The algorithm is compared with six strategies based on movement patterns in terms of three performance models. For the comparison, four scenario types are considered: plain, with obstacles, with the target location probability distribution and a combination of obstacles and the target location probability distribution. For each scenario type, different variations are considered, such as the number of agents and area size. Results show that although simplistic solutions may be convenient for the simplest scenario type, for the more complex ones, the proposed algorithm achieves better results.

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“…There is a need to understand and apply the possibilities allowed by new technologies, e.g. simulating crowds [25], robots in rescuing [26], and so on. ETCs, whose use is not restricted to only academic users and people involved in emergency and rescuing activities, but open to any users with a need for distributed simulation for collaboration are needed.…”

Section: Conclusion and Further Workmentioning

confidence: 99%

Collaboration Patterns in Mixed Reality Environments for a New Emergency Training Center

Backlund

Heldal

Engström

et al. 2013

2013 European Modelling Symposium

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Training actors from public safety agencies (PSA), e.g. emergency medical services, fire departments, police departments involves different technologies and communication and collaboration activities. New technologies promise better support, not only for training, but also for logging relevant information for future analysis and learning. However, choosing the right technologies, defining proper setups for the training activities, and identifying premises for long-term use of technical facilities is both difficult and time consuming. Applying earlier lessons from evaluating work in Virtual Environments (VEs) [1], our aim is to develop a better understanding of the impact of new technologies by identifying collaboration patterns influencing training. Collaboration is examined via social, technical, and task related interaction, distinguishable in the different phases of training, fromstarting an alarm to ending the emergency activities. Our main results illustrate the benefits of (1) building scenarios, and training whole activity chains for certain rescue or other emergency activities, (2) using simulations for better understanding physical places, the task, and (3) distinguishing technical, social and task focused characteristics for factors influencing emergency focused collaboration. Moreover, the results also contribute to understanding the benefits of considering specific simulation technologies when training for emergency and rescue activities.

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Multi-robot exploration and terrain coverage in an unknown environment

Cited by 58 publications

References 16 publications

Multi-Cleaning Robots Using Cleaning Distribution Method Based on Map Decomposition in Large Environments

Multi-Cleaning Robots Using Cleaning Distribution Method Based on Map Decomposition in Large Environments

Comparison of Heuristic Algorithms in Discrete Search and Surveillance Tasks Using Aerial Swarms

Collaboration Patterns in Mixed Reality Environments for a New Emergency Training Center

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