Vision for Mobile Robots

Bartlett, Sandra L.; Hampapur, Arun; Huber, Marcus J.; Kortenkamp, David; Moezzi, S.; Weymouth, Terry E.

doi:10.1007/978-3-642-58288-2_1

Cited by 6 publications

(6 citation statements)

References 60 publications

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“…Traditional approaches to visual robot navigation are usually based on determining the robot position from geometrical maps (reviews can be found in [26,25,3]). Typically, the computational complexity required for these methods by far exceeds the limits that can be assumed for insects.…”

Section: Visual Piloting In Artificial Agentsmentioning

confidence: 99%

A mobile robot employing insect strategies for navigation

Lambrinos

Möller

Labhart

et al. 2000

Robotics and Autonomous Systems

437

335

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The ability to navigate in a complex environment is crucial for both animals and robots. Many animals use a combination of different strategies to return to significant locations in their environment. For example, the desert ant Cataglyphis is able to explore its desert habitat for hundreds of meters while foraging and return back to its nest precisely and on a straight line. The three main strategies that Cataglyphis is using to accomplish this task are path integration, visual piloting and systematic search. In this study, we use a synthetic methodology to gain additional insights into the navigation behavior of Cataglyphis. Inspired by the insect's navigation system we have developed mechanisms for path integration and visual piloting that were successfully employed on the mobile robot Sahabot 2. One the one hand, the results obtained from these experiments provide support for the underlying biological models. On the other hand, by taking the parsimonious navigation strategies of insects as a guideline, computationally cheap navigation methods for mobile robots are derived from the insights gained in the experiments.

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Section: Visual Piloting In Artificial Agentsmentioning

confidence: 99%

A mobile robot employing insect strategies for navigation

Lambrinos

Möller

Labhart

et al. 2000

Robotics and Autonomous Systems

437

335

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“…The algorithm is based on an original psychological model of cognitive mapping applied to a mobile robot [734]. The theory of Stereo and Ego-Motion Mapping applied in this cycle of works is presented in [735], Other works of the same group of authors can be found in [736][737][738].…”

Section: Properties Of Controllersmentioning

confidence: 99%

Intelligent systems:

Meystel¹

2002

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“…An on-board vision system can be used to sense the local information for each robot. Except detecting the neighboring robots (neighbors) and getting their local positions, the vision system can also be used to avoid obstacles along the trajectories [16].…”

Section: Introductionmentioning

confidence: 99%

Formation building and collision avoidance for a fleet of NAOs based on optical sensor with local positions and minimum communication

Xiao-min

Benozzi

Zerr³

et al. 2019

Sci. China Inf. Sci.

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Multi-robot system has become a research hotspot because of low demand on the sensors' accuracy, high reliability, and high efficiency. To put all the robots together, formation control is a crucial problem. In this paper, we propose a local position-based method to plan trajectories and build a pyramid pattern for a fleet of NAOs in the obstacle-free environment by refering to the position-based method and giving an O(n log n) collision avoidance strategy inspired from one graph theory, where the local positions are estimated from optical sensors. To get the local positions, an integrated image processing method is developped. Firstly a mask-base is generated to store the features of NAOs, and a cross-correlation method is introduced to recognize the NAO. Subsequently, the distance and angle models are proposed to get the local information from a single image. Then, a visual compass is introduced to obtain the orientation of one NAO. After the local information exchange by the WiFi communication, a neighbor-check method is put foward to distinguish the homogeneous NAOs (all the NAOs look like the same). Further, a common frame is constructed as an artificial global frame, and straight non-intercrossing trajectories are planned according to the O(n log n) collision avoidance strategy. At last, the performance of our proposed local position-based method is verified by the simulations with up to 15 robots and the indoor experiments with 3 NAOs in a real environment. The convergence of the method has been demonstrated in both obstacle-free and static obstacle environments.

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Vision for Mobile Robots

Cited by 6 publications

References 60 publications

A mobile robot employing insect strategies for navigation

A mobile robot employing insect strategies for navigation

Intelligent systems:

Formation building and collision avoidance for a fleet of NAOs based on optical sensor with local positions and minimum communication

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