The objective of this paper is to present an ongoing development of a context-aware system used within industrial environments. The core of the system is so-called Cognitive Model for Robot Group Control. This model i...
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The objective of this paper is to present an ongoing development of a context-aware system used within industrial environments. The core of the system is so-called Cognitive Model for Robot Group Control. This model is based on well-known concepts of Ubiquitous Computing, and is used to control robot behaviours in specially designed industrial environments. By using sensors integrated within the environment, the system is able to track and analyse changes, and update its informational buffer appropriately. Based on freshly collected information, the Model is able to provide a transformation of high-level contextual information to lower-level information that is much more suitable and understandable for technical systems. The Model uses semantically defined knowledge to define domain of interest, and Bayesian Network reasoning to deal with the uncertain events and ambiguity scenarios that characterize our naturally unstructured world.
In a hybrid road network with multiple paths to same location having prior geographical knowledge, successful navigation for mobile robots is one of the main challenges. Path planning is one of the most important issu...
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In a hybrid road network with multiple paths to same location having prior geographical knowledge, successful navigation for mobile robots is one of the main challenges. Path planning is one of the most important issues in the navigation process which enables the selection and identification of a suitable path for the robot to traverse in the workspace area. Path-planning for mapped roads can be considered as the process of navigating a mobile robot around a configured road map, which provides optimized path by considering roughness of roads. In this paper, we propose a novel navigation algorithm for outdoor environments, which permits robots to travel from one static node to another along a planned path. It utilizes Normal probability weight distribution (NPWD) to assign weights between two nodes dynamically. Heuristics based shortest path (HSP) algorithm is employed to solve complex optimization problems concerned with real-world scenarios. The experiments performed on categorized road databases show significant improvement in timings and complexity of system. Our results justify the effectiveness for the implementation of driver-assist system.
Map-building of mobile robots has long been one of the central research problems in Mobile robotics and spatial cognition. This paper introduces history and related theories of the research on map-building based on pr...
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ISBN:
(纸本)9781424417339
Map-building of mobile robots has long been one of the central research problems in Mobile robotics and spatial cognition. This paper introduces history and related theories of the research on map-building based on probabilistic techniques. It presents and compares several typical map-building methods in detail. The main problems and solutions are also discussed, followed by remarks on future development trends in this area.
Simultaneous localization and mapping (SLAM) is an active area of research. SLAM algorithms should allow the robot to start its movement from a random position in an unknown environment and to build the map of the are...
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Simultaneous localization and mapping (SLAM) is an active area of research. SLAM algorithms should allow the robot to start its movement from a random position in an unknown environment and to build the map of the area while knowing its own position relative to the map. Thus, at the end of the mapping task robot should be able to return where it has started. Especially in real time applications, using limited sensor data, there are still many problems to be conquered. In this study a probabilistic occupancy grid approach is proposed to build the map of an unknown environment. The method tested both in a simulation environment and on a real robot. Although there are some improvements to be made, the initial results are promising.
This paper presents a probabilistic path planning method for robot target search to reduce the expected-time cost in uncertain *** the validity of the manual setting probability decreases with time,a model of attenuat...
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This paper presents a probabilistic path planning method for robot target search to reduce the expected-time cost in uncertain *** the validity of the manual setting probability decreases with time,a model of attenuation and growth is constructed to update the probability information of observation *** different direction may lead to different expected-time in the same loop,a direction choosing method is used to improve the performance of this planning ***,a double-level planning strategy is *** the top level,a heuristic sequence planning algorithm is employed to generate the sequence of observation *** the lower level,the Artificial Potential Field(APF) is applied to plan the optimal path between every two observation *** demonstrated that this method can reduce the expected-time in repeated target search tasks by increasing a little computational cost.
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