Computing high-quality paths in weighted regions

Jaklin, Norman; Tibboel, Mark; Gerærts, Roland

doi:10.1145/2668064.2668097

Cited by 8 publications

(10 citation statements)

References 17 publications

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“…The quality of paths generated with the MIRAN method also depends on the quality of the indicative routes that are used. These can be computed by using A* on a grid (Jaklin, Cook IV, & Geraerts, 2013), but recent approaches rather consider the exact geometry of the environment (Jaklin, Tibboel, & Geraerts, 2014) (see Figure 11).…”

Section: The Modified Indicative Routes and Navigation Methodsmentioning

confidence: 99%

Navigating Through Virtual Worlds

Jaklin

Gerærts

2016

Advances in Game-Based Learning

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With the rise and success of digital games over the past few decades, path planning algorithms have become an important aspect in modern game development for all types of genres. Indirectly-controlled playable characters as well as non-player characters have to find their way through the game's environment to reach their goal destinations. Modern gaming hardware and new algorithms enable the simulation of large crowds with thousands of individual characters. Still, the task of generating feasible and believable paths in a time- and storage-efficient way is a big challenge in this emerging and exciting research field. In this chapter, the authors describe classical algorithms and data structures, as well as recent approaches that enable the simulation of new and immersive features related to path planning and crowd simulation in modern games. The authors discuss the pros and cons of such algorithms, give an overview of current research questions and show why graph-based methods will soon be replaced by novel approaches that work on a surface-based representation of the environment.

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Section: The Modified Indicative Routes and Navigation Methodsmentioning

confidence: 99%

Navigating Through Virtual Worlds

Jaklin

Gerærts

2016

Advances in Game-Based Learning

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“…The problem is conjectured to be computationally hard, thus the focus of the community has been on approximate algorithms. For an overview of recent results, as well as a generalization of the problem, see (Sun and Reif 2007;Jaklin, Tibboel, and Geraerts 2014).…”

Section: Planning In Low Dimensionsmentioning

confidence: 99%

Efficient Motion Planning for Problems Lacking Optimal Substructure

Salzman

Hou

Srinivasa

2017

ICAPS

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We consider the motion-planning problem of planning a collision-free path of a robot in the presence of risk zones. The robot is allowed to travel in these zones but is penalized in a super-linear fashion for consecutive accumulative time spent there. We suggest a natural cost function that balances path length and risk-exposure time. Specifically, we consider the discrete setting where we are given a graph, or a roadmap, and we wish to compute the minimal-cost path under this cost function. Interestingly, paths defined using our cost function do not have an optimal substructure. Namely, subpaths of an optimal path are not necessarily optimal. Thus, the Bellman condition is not satisfied and standard graph-search algorithms such as Dijkstra cannot be used. We present a path-finding algorithm, which can be seen as a natural generalization of Dijkstra’s algorithm. Our algorithm runs in O ((n B · n) log(n B · n) + n B · m) time, where n and m are the number of vertices and edges of the graph, respectively, and n B is the number of intersections between edges and the boundary of the risk zone. We present simulations on robotic platforms demonstrating both the natural paths produced by our cost function and the computational efficiency of our algorithm.

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“…Hence, ignoring other factors, the this method can run, in reasonable time, with a very-close optimum result. However, when all factors involved in the time complexity, this method is considered as a theoretical method rather than a practical one (Szczerba, Chen, and John J. Uhran 1998;Jaklin, Tibboel, and Geraerts 2014). Furthermore, the algorithms by (Rowe and Alexander 2000;Rowe and Richbourg 1990) are similar in that they exploit Snell's law, but have been evaluated as unrealistic methods (Szczerba, Chen, and John J. Uhran 1998).…”

Section: Introductionmentioning

confidence: 99%

Computing Close to Optimal Weighted Shortest Paths in Practice

Tran

Dinneen

Linz

2020

ICAPS

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This paper proposes a new practical method for the weighted region problem (WRP). The objective of WRP is to find a minimum cost path between two vertices among different regions where each region incurs a traversal cost per unit distance. Currently, there is no practical algorithm that solves this problem exactly. Among the approximation methods that solve instances of WRP, there is a limited number of algorithms that compute paths whose lengths are close to optimal, which we call very-close optimum paths. However, they are considered as theoretical methods. On the other hand, algorithms for solving WRP that can be applied to practical data sets (using decomposition ideas or heuristics) are not guaranteed to find a very-close optimum path within an acceptable amount of time. In this paper, we consider an alternative method for solving WRP that exploits Snell's law of physical refraction. We compare the performance of our new algorithm with that of two existing algorithms, using at least 500 test cases for each such comparison. The experimental results show that our algorithm returns a very-close optimum weighted shortest path in reasonable time.

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Computing high-quality paths in weighted regions

Cited by 8 publications

References 17 publications

Navigating Through Virtual Worlds

Navigating Through Virtual Worlds

Efficient Motion Planning for Problems Lacking Optimal Substructure

Computing Close to Optimal Weighted Shortest Paths in Practice

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