Multi-Agent Path Finding (MAPF) is the problem of finding a set of collision-free paths for multiple agents in a shared environment while improving the solution quality. The state-of-the-art anytime MAPF algorithm is ...
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ISBN:
(纸本)9798400704864
Multi-Agent Path Finding (MAPF) is the problem of finding a set of collision-free paths for multiple agents in a shared environment while improving the solution quality. The state-of-the-art anytime MAPF algorithm is based on Large Neighborhood Search (MAPF-LNS), which is a combinatorial search algorithm that iteratively destroys and repairs a subset of collision-free paths. In this paper, we propose Destroy-Repair Operation Parallelism for MAPF-LNS (DROP-LNS), a parallel framework that performs multiple destroy and repair operations concurrently to explore more regions of the search space and improve the solution quality. Unlike MAPF-LNS, DROP-LNS is able to exploit multiple threads during the search. The results show that DROP-LNS outperforms the state-of-the-art anytime MAPF algorithms, namely MAPF-LNS and LaCAM*, with respect to solution quality when terminated at the same runtime.
One key challenge in SVM classification is to develop training algorithms to handle large data sets in a cost-effective way. anytime algorithm offers users a sequence of useful results and allows them to harness compu...
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One key challenge in SVM classification is to develop training algorithms to handle large data sets in a cost-effective way. anytime algorithm offers users a sequence of useful results and allows them to harness computational cycles based on their own available resources. Once an initial approximate result is produced, the algorithm can be interrupted and output a result at anytime. If more computations are conducted, the algorithm can continue improving the quality of result. So far, conventional support vector machine(SVM) training algorithm is a deterministic process that only produces an "all-or-nothing" result. In this paper, we develop an anytime Programming Library(APL) to simplify the development of anytime SVM training algorithms. The effectiveness of APL is demonstrated by developing three different anytime SVM training algorithms and experiments were conducted to evaluate the effectiveness of these algorithms.
The free-climbing robots are the alternative to wheeled vehicles, because the legged locomotion allows mobility on the steep gradients under the harsh environmental conditions. The steady progression requires coordina...
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The free-climbing robots are the alternative to wheeled vehicles, because the legged locomotion allows mobility on the steep gradients under the harsh environmental conditions. The steady progression requires coordination of leg movements for which the force sensors implemented in the joints of the robot legs are not always sufficient. Considering the previous developments in the field of robotics, the combination of the impedance and vision sensors comprises an obvious solution for providing stable and accurate contact between the individual legs and the ground. The implementation of additional, non-invasive sensor/s on all the legs of the hexapod robotic platform, for the indirect measuring and analysis of the terrain surface, is investigated. Furthermore, a method for the parametric 3D modeling of simulation assemblies is described in a mixed top-down, bottom-up and middle-out design approach. Mechanisms were created using the common origin skeletal modeling (top-down approach), allowing the context analysis and the definition of the parameters for the further numerical optimization in an early phase of the development. Finally, the applicability of the anytime approach during the optimization of the mechanical parts of the mechanism in the design process was tested.
This paper addresses the challenges of real-time, large-scale, and near-optimal multi-agent pathfinding (MAPF) through enhancements to the recently proposed LaCAM* algorithm. LaCAM* is a scalable search-based algorith...
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ISBN:
(纸本)9798400704864
This paper addresses the challenges of real-time, large-scale, and near-optimal multi-agent pathfinding (MAPF) through enhancements to the recently proposed LaCAM* algorithm. LaCAM* is a scalable search-based algorithm that guarantees the eventual finding of optimal solutions for cumulative transition costs. While it has demonstrated remarkable planning success rates, surpassing various state-of-the-art MAPF methods, its initial solution quality is far from optimal, and its convergence speed to the optimum is slow. To overcome these limitations, this paper introduces several improvement techniques, partly drawing inspiration from other MAPF methods. We provide empirical evidence that the fusion of these techniques significantly improves the solution quality of LaCAM*, thus further pushing the boundaries of MAPF algorithms.
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