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However, rapid improvements in computer vision, processing power and storage, low-cost sensors, as well as new algorithms that allow robots to plan and move in cluttered environments, are making these new uses possible and in the process changing the nature of robotics.
We experimentally validate the effectiveness and feasibility of object categorization in cluttered environments.
Operations of transportation in cluttered environments require robust motion planning algorithms.
However, as both variants perform pure exploration, they tend to suffer in highly cluttered environments.
Moreover, in indoor or cluttered environments its use is precluded entirely.
This paper presents a novel reactive collision avoidance method for mobile robots moving in dense and cluttered environments.
Robot Navigation in unknown and very cluttered environments constitutes one of the key challenges in unmanned ground vehicle (UGV) applications.
When classical localization devices failed (e.g. GPS sensor in cluttered environments), aforementioned data could be used within a localization framework.
We know a lot about how birds navigate over long distances, yet little about how they steer through small-scale, cluttered environments like forests and city streets.
Whether the localization techniques are based on signal strength or on signal time-of-flight, measurement errors are unavoidable, especially in cluttered environments.
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It is thus likely that they adapt emission levels to the echo reflection properties of the environment to some degree; fainter in confined, echo-cluttered environments and louder in more open, less cluttered situations.
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