Autonomous navigation in a known dynamic environment

Kiss, L.; Várkonyi-Kóczy, Annamária R.; Baranyi, N.

doi:10.1109/fuzz.2003.1209373

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…The surveys [7,8] give a general overview on anytime methods. Methods based on A* can be connected with other approaches, the studies [9,10] investigated scenarios where A* was used as a global planning algorithm and local modifications were carried out based on potential fields method.…”

Section: Related Workmentioning

confidence: 99%

Anytime Reactive Planning with Decision Automation

Komlósi

2015

AMR

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In many outdoor robotic applications several factors have to be taken into account during a path planning process. In different situations the importance of these factors vary. This paper presents a path planning method for mobile robots that incorporates decision theory to guide the search. A neural structure is proposed to determine the relative importance of the objectives that makes the robot capable of planning in unfamiliar situations. The method is able to handle an arbitrary number of objectives simultaneously and also enables the incorporation of human logic into the planning process. All parts of the algorithm suit real-time implementation.

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Section: Related Workmentioning

confidence: 99%

Anytime Reactive Planning with Decision Automation

Komlósi

2015

AMR

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“…The used approaches can be divided into the classes of functional/cybernetic, behavioristic, and hybrid approaches. A typical functional approach can, for example, be found in [1,2], where global and local planning modules work on a 2D geometrical and topological map to plan subgoals for the mobile systems, using current sensor data to adapt paths while targeting the subgoals. A behaviorbased approach is, for example, given in [3][4][5], where a set of logical and physical sensor systems is situation-dependent activated to search for edges or obstacles.…”

Section: Autonomous Onboard Navigationmentioning

confidence: 99%

Autonomous Robot Navigation in Human-Centered Environments Based on 3D Data Fusion

Steinhaus

Strand

Dillmann

2007

EURASIP J. Adv. Signal Process.

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Efficient navigation of mobile platforms in dynamic human-centered environments is still an open research topic. We have already proposed an architecture (MEPHISTO) for a navigation system that is able to fulfill the main requirements of efficient navigation: fast and reliable sensor processing, extensive global world modeling, and distributed path planning. Our architecture uses a distributed system of sensor processing, world modeling, and path planning units. In this arcticle, we present implemented methods in the context of data fusion algorithms for 3D world modeling and real-time path planning. We also show results of the prototypic application of the system at the museum ZKM (center for art and media) in Karlsruhe.

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A Universal Autonomous Robot Navigation Method

Várkonyi-Kóczy¹

2008

J. Adv. Comput. Intell. Intell. Inform.

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Recently, autonomous navigation has become an important research topic. There are a lot of applications where the need for autonomous robots is obvious, either because the real or virtual human presence is impossible, dangerous, or expensive, or the tasks to be solved are against the human nature. In most of the applications where robots are to be used, the conditions/environment change along the time that results in an ever-increasing need for universal methods, which are general enough to be used at a wide range of problems. In this paper, a universal, hybrid navigation method is proposed, which is able to work in cases of known, partially known, dynamically changing, or unknown environments. The model consists of two parts which are able to co-operate or to work alone. The modules combine two techniques that deal with a priori information and sensory data separately, thus blends the intelligence and optimality of global navigation methods with the reactivity and low complexity of local ones. The first, global navigation module, based on a priori information, chooses intermediary goals for the local navigation module, for which the so called A* algorithm is used. The second part, carrying out the (local) navigation relying on sensory data, applies a fuzzy-neural representation of an improved potential field based guiding navigation tool. Vision based obstacle detection is implemented by difference detection based on a combination of RGB and HSV representations of the pixels.

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Autonomous navigation in a known dynamic environment

Cited by 4 publications

References 14 publications

Anytime Reactive Planning with Decision Automation

Anytime Reactive Planning with Decision Automation

Autonomous Robot Navigation in Human-Centered Environments Based on 3D Data Fusion

A Universal Autonomous Robot Navigation Method

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