One of the main problems in the navigation of robotic swarms is when several robots try to reach the same target at the same time, causing congestion situations that may compromise performance. In this paper, we propo...
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One of the main problems in the navigation of robotic swarms is when several robots try to reach the same target at the same time, causing congestion situations that may compromise performance. In this paper, we propose a distributed coordination algorithm to alleviate this type of congestion. Using local sensing and communication, and controlling their actions using a probabilistic finite state machine, robots are able to coordinate themselves to avoid these situations. Simulations and real experiments were executed to study the performance and effectiveness of the proposed algorithm. Results show that the algorithm allows the swarm to have a more efficient and smoother navigation and is suitable for large groups of robots.
A very common problem in the navigation of robotic swarms is when groups of robots move into opposite directions, causing congestion situations that may compromise performance. In this paper, we propose a distributed ...
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A very common problem in the navigation of robotic swarms is when groups of robots move into opposite directions, causing congestion situations that may compromise performance. In this paper, we propose a distributed coordination algorithm to alleviate this type of congestion. By working collaboratively, and warning their teammates about a congestion risk, robots are able to coordinate themselves to avoid these situations. We executed simulations and real experiments to study the performance and effectiveness of the proposed algorithm. Results show that the algorithm allows the swarm to navigate in a smoother and more efficient fashion, and is suitable for large groups of robots.
In this paper we present a methodology based on a variation of the spatial pythagorean hodograph curves to generate smooth feasible paths for autonomous vehicles in three-dimensional space under the restriction of lim...
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In this paper we present a methodology based on a variation of the spatial pythagorean hodograph curves to generate smooth feasible paths for autonomous vehicles in three-dimensional space under the restriction of limited climb angles. An fast iterative algorithm is used to calculate the curve. The generated path satisfy three main angular constraints given by the vehicle: (i) maximum curvature, (ii) maximum torsion and (iii) maximum climb (or dive). A path is considered feasible if these kinematic constraints are not violated. The smoothness vehicle's acceleration profile is indirectly guaranteed between two points. The proposed methodology is applicable to vehicles that move in three-dimensional environments, and that can be modeled by the constraints considered here. We show results for small aerial vehicle.
We present an algorithm that allows swarms of robots to navigate in environments containing unknown obstacles, moving towards and spreading along 2D shapes given by implicit functions. Basically, a gradient descent ap...
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In this paper, we address navigation and coordination methods that allow swarms of robots to converge and spread along complex 2D shapes in environments containing unknown obstacles. Shapes are modeled using implicit ...
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In this paper, we address navigation and coordination methods that allow swarms of robots to converge and spread along complex 2D shapes in environments containing unknown obstacles. Shapes are modeled using implicit functions and a gradient descent approach is used for controlling the swarm. To overcome local minima, that may appear in these scenarios, we use a coordination mechanism that reallocates some robots as "rescuers" and sends them to help other robots that may be trapped. Simulations and real experiments demonstrate the feasibility of the proposed approach.
In this paper we study the use of computervision techniques for for underwater visual tracking and counting of fishes in vivo. The methodology is based on the application of a Bayesian filtering technique that enable...
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In this paper we study the use of computervision techniques for for underwater visual tracking and counting of fishes in vivo. The methodology is based on the application of a Bayesian filtering technique that enable...
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In this paper we study the use of computervision techniques for for underwater visual tracking and counting of fishes in vivo. The methodology is based on the application of a Bayesian filtering technique that enables tracking of objects whose number may vary over time. Unlike existing fish-counting methods, this approach provides adequate means for the acquisition of relevant information about characteristics of different fish species such as swimming ability, time of migration and peak flow rates. The system is also able to estimate fish trajectories over time, which can be further used to study their behaviors when swimming in regions of interest. Our experiments demonstrate that the proposed method can operate reliably under severe environmental changes (e.g. variations in water turbidity) and handle problems such as occlusions or large inter-frame motions. The proposed approach was successfully validated with real-world video streams, achieving overall accuracy as high as 81%.
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