Experimental Comparison of A* and D* Lite Path Planning Algorithms for Differential Drive Automated Guided Vehicle

Setiawan, Yuhanes Dedy; Pratama, Pandu Sandi; Jeong, Sang Kwon; Duy, Vo Hoang; Kim, Sang Bong

doi:10.1007/978-3-642-41968-3_55

Cited by 19 publications

(7 citation statements)

References 3 publications

(2 reference statements)

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“…In this scenario D* Lite provides superior performance compared to A* as it has to do minimal recalculation to update the paths every time the graph changes. This increase in computational efficiency of D* Lite over A* has also been replicated in other experiments [34], [35].…”

Section: Advantages Of Using D* Lite Over A*supporting

confidence: 65%

Dynamic Prioritization for Conflict-Free Path Planning of Multi-Robot Systems

Rathi,

Vadali

2021

Preprint

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Planning collision-free paths for multi-robot systems (MRS) is a challenging problem because of the safety and efficiency constraints required for real-world solutions. Even though coupled path planning approaches provide optimal collision-free paths for each agent of the MRS, they search the composite space of all the agents and therefore, suffer from exponential increase in computation with the number of robots. On the other hand, prioritized approaches provide a practical solution to applications with large number of robots, especially when path computation time and collision avoidance take precedence over guaranteed globally optimal solution. While most centrally-planned algorithms use static prioritization, a dynamic prioritization algorithm, PD*, is proposed that employs a novel metric, called freedom index, to decide the priority order of the robots at each time step. This allows the PD* algorithm to simultaneously plan the next step for all robots while ensuring collision-free operation in obstacle ridden environments. Extensive simulations were performed to test and compare the performance of the proposed PD* scheme with other state-of-the-art algorithms. It was found that PD* improves upon the computational time by 25% while providing solutions of similar path lengths. Increase in efficiency was particularly prominent in scenarios with large number of robots and/or higher obstacle densities, where the probability of collisions is higher, suggesting the suitability of PD* in solving such problems.

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Section: Advantages Of Using D* Lite Over A*supporting

confidence: 65%

Dynamic Prioritization for Conflict-Free Path Planning of Multi-Robot Systems

Rathi,

Vadali

2021

Preprint

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“…Many researchers have used many representative algorithms to solve the path planning problem of AGV, including graph-based search algorithms such as Dijkstra [3], A* [4,5], D* [6], etc. These algorithms have the advantage of being simple and easy to implement and have been widely used in AGV path planning.…”

Section: Introductionmentioning

confidence: 99%

Improved Dyna-Q: A Reinforcement Learning Method Focused via Heuristic Graph for AGV Path Planning in Dynamic Environments

Liu

Yan

Zhao

et al. 2022

Drones

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Dyna-Q is a reinforcement learning method widely used in AGV path planning. However, in large complex dynamic environments, due to the sparse reward function of Dyna-Q and the large searching space, this method has the problems of low search efficiency, slow convergence speed, and even inability to converge, which seriously reduces the performance and practicability of it. To solve these problems, this paper proposes an Improved Dyna-Q algorithm for AGV path planning in large complex dynamic environments. First, to solve the problem of the large search space, this paper proposes a global path guidance mechanism based on heuristic graph, which can effectively reduce the path search space and, thus, improve the efficiency of obtaining the optimal path. Second, to solve the problem of the sparse reward function in Dyna-Q, this paper proposes a novel dynamic reward function and an action selection method based on the heuristic graph, which can provide more intensive feedback and more efficient action decision for AGV path planning, effectively improving the convergence of the algorithm. We evaluated our approach in scenarios with static obstacles and dynamic obstacles. The experimental results show that the proposed algorithm can obtain better paths more efficiently than other reinforcement-learning-based methods including the classical Q-Learning and the Dyna-Q algorithms.

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“…5,6 Such searching algorithms also include D* algorithms which can dynamically reconstruct map for moving obstacles. 7 RRT and probabilistic road maps are algorithms based on sampling, ensuring probabilistic completeness. 8 This means with enough iteration times, the probability of finding path, if exists, approaches one.…”

Section: Introductionmentioning

confidence: 99%

A new path planning method based on concave polygon convex decomposition and artificial bee colony algorithm

Zhang

Liu

et al. 2020

International Journal of Advanced Robotic Systems

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Free space algorithms are kind of graphics-based methods for path planning. With previously known map information, graphics-based methods have high computational efficiency in providing a feasible path. However, the existing free space algorithms do not guarantee the global optimality because they always search in one connected domain but not all the possible connected domains. To overcome this drawback, this article presents an improved free space algorithm based on map decomposition with multiple connected domains and artificial bee colony algorithm. First, a decomposition algorithm of single-connected concave polygon is introduced based on the principle of concave polygon convex decomposition. Any map without obstacle is taken as single-connected concave polygon (the convex polygon map can be seen as already decomposed and will not be discussed here). Single concave polygon can be decomposed into convex polygons by connecting concave points with their visible vertex. Second, decomposition algorithm for multi-connected concave polygon (any map with obstacles) is designed. It can be converted into single-connected concave polygon by excluding obstacles using virtual links. The map can be decomposed into several convex polygons which form multiple connected domains. Third, artificial bee colony algorithm is used to search the optimal path in all the connected domains so as to avoid falling into the local minimum. Numerical simulations and comparisons with existing free space algorithm and rapidly exploring random tree star algorithm are carried out to evaluate the performance of the proposed method. The results show that this method is able to find the optimal path with high computational efficiency and accuracy. It has advantages especially for complex maps. Furthermore, parameter sensitivity analysis is provided and the suggested values for parameters are given.

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Experimental Comparison of A* and D* Lite Path Planning Algorithms for Differential Drive Automated Guided Vehicle

Cited by 19 publications

References 3 publications

Dynamic Prioritization for Conflict-Free Path Planning of Multi-Robot Systems

Dynamic Prioritization for Conflict-Free Path Planning of Multi-Robot Systems

Improved Dyna-Q: A Reinforcement Learning Method Focused via Heuristic Graph for AGV Path Planning in Dynamic Environments

A new path planning method based on concave polygon convex decomposition and artificial bee colony algorithm

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