Robust and Computationally Efficient Navigation in Domestic Environments

Holz, Dirk; Kraetzschmar, Gerhard K.; Rome, Erich

doi:10.1007/978-3-642-11876-0_10

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…More recently, some very successful approaches, e.g., [10] and [11], have made significant efforts in integrating efficient path planning and obstacle avoidance methods for navigation of domestic service robots in home environments. Apart from being robust and reliable, one very interesting property of these integrated methods is that they automatically account for human presence in the environment and do not depend on any heuristics.…”

Section: Related Workmentioning

confidence: 99%

Towards Optimal Robot Navigation in Domestic Spaces

Ventura

Ahmad

2015

RoboCup 2014: Robot World Cup XVIII

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The work presented in this paper is motivated by the goal of dependable autonomous navigation of mobile robots. This goal is a fundamental requirement for having autonomous robots in spaces such as domestic spaces and public establishments, left unattended by technical staff. In this paper we tackle this problem by taking an optimization approach: on one hand, we use a Fast Marching Approach for path planning, resulting in optimal paths in the absence of unmapped obstacles, and on the other hand we use a Dynamic Window Approach for guidance. To the best of our knowledge, the combination of these two methods is novel. We evaluate the approach on a real mobile robot, capable of moving at high speed. The evaluation makes use of an external ground truth system. We report controlled experiments that we performed, including the presence of people moving randomly nearby the robot. In our long term experiments we report a total distance of 18 km traveled during 11 hours of movement time.

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Section: Related Workmentioning

confidence: 99%

Towards Optimal Robot Navigation in Domestic Spaces

Ventura

Ahmad

2015

RoboCup 2014: Robot World Cup XVIII

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“…It uses SLAM based on Iterative Closest Point (ICP) techniques [96]. It receives the sensor readings from the laser range finder and odometry for its localisation and mapping tasks.…”

Section: Software Componentsmentioning

confidence: 99%

A Hybrid Deliberative Layer for Robotic Agents

Hartanto

2011

Lecture Notes in Computer Science

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Cognitive robots are all over the place. Well, at least that is what it looks like in research lab agendas on the planet.Some basic and fundamental problems in controlling mobile robots have been quite well understood in recent years: Localization and map building are chief examples. Now, as autonomous mobility as such is much less of a challenge, research resumes an activity that has started about half a century ago in mainstream AI; that got out of the AI mainstream for a while, as other challenges turned out to offer solutions faster; that got a little mocked for some time ("good old-fashioned AI", gofai); yet that has remained a fascinating scientific problem, and potentially a rich source of application projects: algorithmic models for goal-directed physical action in the world.So in a sense, very recent and ambitious projects in cognitive robotics, or cognitive technical systems, or robot cognition, or whatever you prefer to call them, are taking up that thread, which, I would say, goes back to the pioneering shakey project in the AI of the 1960s, and has remained unresolved ever since. However, there are a number of interesting differences between thinking about a cognitive robot in the 1960s and doing so today: Our modern computers are nearly infinitely more powerful; sensors are much better; decent robot hardware has become affordable even for a normal university lab; and, the proper share of AI: 50 years of AI research has yielded quite a number of wellunderstood algorithms and formalisms for planning, knowledge representation and reasoning that were unknown in the early days.And still, getting a cognitive robot to work is a challenge -which is why cognitive robots are not yet all over the place, and the cognition in those that are may fail to impress. Among a zoo of problems from "just" technical (integration!) to big AI problem (symbol grounding!) is a specimen that has to do with the proper AI share of the story: Using a planner or reasoner within the control software of an autonomous cognitive robot is different from using them in an off-line or human-in-the-loop system. Now this is the point where Ronny Hartanto's thesis comes into play. A robot working, for example, in a mundane household domain, would need to May 2011Joachim Hertzberg PrefaceAction planning has been used in the field of robotics for solving long-running tasks. In the robot architectures field, it is also known as the deliberative layer. However, there is still a gap between the symbolic representation on the one hand and the low-level control and sensor representation on the other. In addition, the definition of a planning problem for a complex, real-world robot is not trivial. The planning process could become intractable as its search spaces become large. Since the defined planning problem determines the complexity and the computability for solving the problem, it should contain only relevant states. In this work, a novel approach which amalgamates description logic (dl) reasoning with hierarchical task network (htn) planning i...

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Robust and Computationally Efficient Navigation in Domestic Environments

Cited by 2 publications

References 18 publications

Towards Optimal Robot Navigation in Domestic Spaces

Towards Optimal Robot Navigation in Domestic Spaces

A Hybrid Deliberative Layer for Robotic Agents

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