Path planning in changing environments with difficult regions, such as narrow passages and obstacle boundaries, creates significant chal- lenges. As the obstacles in W-space move frequently, the crowd degree of C-spac...Path planning in changing environments with difficult regions, such as narrow passages and obstacle boundaries, creates significant chal- lenges. As the obstacles in W-space move frequently, the crowd degree of C-space changes accordingly. Therefore, in order to dynamically improve the sampling quality, it is appreciated for a planner to rapidly approximate the crowd degree of different parts of the C-space, and boost sample densities with them based on their difficulty levels. In this paper, a novel approach called Adaptive Region Boosting （ARB） is proposed to increase the sampling density for difficult areas with different strategies. What＇s more, a new criterion, called biased entropy, is proposed to evaluate the difficult degree of a region. The new criterion takes into account both temporal and spatial information of a specific C-space region, in order to make a thorough assessment to a local area. Three groups of experiments are conducted based on a dual-manipulator system with 12 DoFs. Experimental results indicate that ARB effectively improves the success rate and outperforms all the other related methods in various dynamical scenarios.展开更多
Nowadays the actual parking situation is becoming more and more complex. Because of the inexpert driving skill of novice drivers, there is often collision in the parking process. Fortunately, with the development of p...Nowadays the actual parking situation is becoming more and more complex. Because of the inexpert driving skill of novice drivers, there is often collision in the parking process. Fortunately, with the development of path planning and intelligent control technology, the appearance of the automatic parking control system will provide a good solution to the narrow parking problems. Here with the help of MATLAB/Simulink, we build a simulation platform, and then respectively simulate the parallel parking and vertical parking process, to seek for the best initial parking point and path.展开更多
Case-based reasoning is an AI technique in which the previous solutions are stored for future use. People are used to guiding themselves according to those routes that are stored in their memories and have been used b...Case-based reasoning is an AI technique in which the previous solutions are stored for future use. People are used to guiding themselves according to those routes that are stored in their memories and have been used by them before. It is just based on people＇s preference to familiar routes, which are gained through the study of the cognitive activities. We propose to apply the intelligent method based on the case reasoning to path planning. It is impossible for a case base to store all the solutions to all the shortest paths; therefore, part of them should be stored. However, which routes should be stored and which should not be？ How do we adapt the cases that have already been stored and how do we acquire the shortest route based on them？ All these issues need to be explained by integrating knowledge of the network on account of case-based reasoning techniques. This paper suggests the case-based reasoning in another point. This means finding some irreplaceable links on the basis of the complete analysis of the problems space, which are called the must_be_passed link between the source and destination. Merely compute the shortest path case from those best exit/entry nodes of the grids to the irreplaceable links, and then add them into the case base storing for future use. This method is based on case-based reasoning technique and completely considers the properties of the problem space. In addition to the use of knowledge of the natural grid in the route network, this method is more efficient than existing algorithms on computing efficiency.展开更多
文摘Path planning in changing environments with difficult regions, such as narrow passages and obstacle boundaries, creates significant chal- lenges. As the obstacles in W-space move frequently, the crowd degree of C-space changes accordingly. Therefore, in order to dynamically improve the sampling quality, it is appreciated for a planner to rapidly approximate the crowd degree of different parts of the C-space, and boost sample densities with them based on their difficulty levels. In this paper, a novel approach called Adaptive Region Boosting （ARB） is proposed to increase the sampling density for difficult areas with different strategies. What＇s more, a new criterion, called biased entropy, is proposed to evaluate the difficult degree of a region. The new criterion takes into account both temporal and spatial information of a specific C-space region, in order to make a thorough assessment to a local area. Three groups of experiments are conducted based on a dual-manipulator system with 12 DoFs. Experimental results indicate that ARB effectively improves the success rate and outperforms all the other related methods in various dynamical scenarios.
文摘Nowadays the actual parking situation is becoming more and more complex. Because of the inexpert driving skill of novice drivers, there is often collision in the parking process. Fortunately, with the development of path planning and intelligent control technology, the appearance of the automatic parking control system will provide a good solution to the narrow parking problems. Here with the help of MATLAB/Simulink, we build a simulation platform, and then respectively simulate the parallel parking and vertical parking process, to seek for the best initial parking point and path.
基金Supported by the National 863 program of China (No. 2006AA12Z202)
文摘Case-based reasoning is an AI technique in which the previous solutions are stored for future use. People are used to guiding themselves according to those routes that are stored in their memories and have been used by them before. It is just based on people＇s preference to familiar routes, which are gained through the study of the cognitive activities. We propose to apply the intelligent method based on the case reasoning to path planning. It is impossible for a case base to store all the solutions to all the shortest paths; therefore, part of them should be stored. However, which routes should be stored and which should not be？ How do we adapt the cases that have already been stored and how do we acquire the shortest route based on them？ All these issues need to be explained by integrating knowledge of the network on account of case-based reasoning techniques. This paper suggests the case-based reasoning in another point. This means finding some irreplaceable links on the basis of the complete analysis of the problems space, which are called the must_be_passed link between the source and destination. Merely compute the shortest path case from those best exit/entry nodes of the grids to the irreplaceable links, and then add them into the case base storing for future use. This method is based on case-based reasoning technique and completely considers the properties of the problem space. In addition to the use of knowledge of the natural grid in the route network, this method is more efficient than existing algorithms on computing efficiency.
