Multi agents' multi targets mission under uncertainty using probability navigation function

Hacohen, Shlomi; Shoval, Shraga; Shvalb, Nir

doi:10.1109/icca.2017.8003170

Cited by 10 publications

(10 citation statements)

References 14 publications

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“…In the literature, a swarm commonly refers to simple physical agents with minimal communication needs (or having no communication needs as in [36]). This applies to ants, up to the point of global motion planning [2]:…”

Section: B Communicationmentioning

confidence: 99%

Robotic Swarm Motion Planning for Load Carrying and Manipulating

2020

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Certain species of ants can carry out tasks in dense work spaces while maintaining their ability to accurately manipulate heavy loads, and these advantages are of interest to the robotics community. We consider a robotic swarm of N ≥ 6 agents that assumes the task of moving a load through a cluttered space. This forces the swarm to carefully manipulate the orientation of the load, while transporting it to its destination point. We model this scenario as a 6-PPSS (Prismatic-Prismatic-Spherical-Spherical) redundant mobile platform, having six degrees of freedom. As with insects, the multitude of agents enables sharing the burden of the load in the case that one or more agents are blocked by an obstacle. We model this by a semi-algebraic set of constraints on the distances between the agents and the load. We apply an Extended Kalman Filter routine, in order to estimate their relative locations. We show how the estimation-error is reduced when position-information is shared among the agents. These estimations are then used to calculate the full configuration and investigate the effect of position estimation error on the platform heading error. We show how motion planning can then be calculated in the model's full configuration space and demonstrate this with a distributed control scheme. To reduce the search time, we introduce a variant of the crawling probabilistic road map motion planning algorithm under a set of kinematic constraints and work-space obstacles. Finally, we exemplify our algorithms on several simulated scenarios. INDEX TERMS Swarm, load, extended Kalman filter, parallel platform, crawling probabilistic road map.

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Section: B Communicationmentioning

confidence: 99%

Robotic Swarm Motion Planning for Load Carrying and Manipulating

2020

Self Cite

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“…The robot must have enough liberty to be capable of performing its actions, but it must comply with the aims of the swarm. Some rovers can follow a direction [29], perform mechanical actions [30], and measure environmental conditions [31], while some others can assign goals and trajectories [19][20][21]. In this article, our swarm should be able to coordinate all these actions while following a group of migrants.…”

Section: Related Workmentioning

confidence: 99%

Toward a robot swarm protecting a group of migrants

Vaidis

Otis

2020

Intel Serv Robotics

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Different geopolitical conflicts of recent years have led to mass migration of several civilian populations. These migrations take place in militarized zones, indicating real danger contexts for the populations. Indeed, civilians are increasingly targeted during military assaults. Defense and security needs have increased; therefore, there is a need to prioritize the protection of migrants. Very few or no arrangements are available to manage the scale of displacement and the protection of civilians during migration. In order to increase their security during mass migration in an inhospitable territory, this article proposes an assistive system using a team of mobile robots, labeled a rover swarm that is able to provide safety area around the migrants. We suggest a coordination algorithm including CNN and fuzzy logic that allows the swarm to synchronize their movements and provide better sensor coverage of the environment. Implementation is carried out using on a reduced scale rover to enable evaluation of the functionalities of the suggested software architecture and algorithms. Results bring new perspectives to helping and protecting migrants with a swarm that evolves in a complex and dynamic environment.

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“…Hacohen and al. [9] have created a swarm capable of intercepting targets which are not desirable in a surveyed zone. In [8], the robot swarm also allows the survey of the zone with the aim of finding prey for hunt.…”

Section: Tasks To Domentioning

confidence: 99%

“…Hacohen and al. [9] have developed a probabilistic navigation algorithm for mobile robots. The positions of all objects are considered as random variables.…”

Section: Algorithm Support Vector Machines (Svm)mentioning

confidence: 99%

Human-Rover Interactions and Swarm Algorithms of Mobile Robots in an Open and Crowded Environment: A Survey

Vaidis¹,

Otis²

2020

Preprint

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As a result of extensive research in the field of mobile robots (rovers) and swarms, a number of algorithms exist to assist them for executing a mission in the three levels of software architecture: strategic (interaction loop level), tactic (planning) and operational (sensing, control and actuation). They allow them to achieve their goals while adapting to their environment through a multitude of methods designed for each situation. For this reason, a literature review of the latest research conducted in previous years is required to identify new research trends in human-swarm interaction applied to help humans in hazardous environment such as militarized zone. In this paper, we will present some interesting algorithms for interactive and autonomous mobile robots acting in swarms in an open and crowded environment. A discussion will focus on comparing different algorithms and their advantages and disadvantages.

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Multi agents' multi targets mission under uncertainty using probability navigation function

Cited by 10 publications

References 14 publications

Robotic Swarm Motion Planning for Load Carrying and Manipulating

Robotic Swarm Motion Planning for Load Carrying and Manipulating

Toward a robot swarm protecting a group of migrants

Human-Rover Interactions and Swarm Algorithms of Mobile Robots in an Open and Crowded Environment: A Survey

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