Multitarget Real-Time Path Planning Using Double Adaptive A Algorithm

Pu, Xiandong; Zhang, Jianlei

doi:10.1109/taes.2023.3241120

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…In response to issues such as low search efficiency and high memory overhead in the traditional A* algorithm, various classic variants and improvements of A* have emerged, including Weighted A* (WA*) [53], Adaptive A* (AA*) [54], Theta* [55], and Jump Point Search (JPS) [56], as depicted in Table 7 and Figure 8. Pu et al [57] introduced a dual adaptive A* algorithm, which encompasses adaptive multi-objective heuristic functions and adaptive node expansion strategies. Lai et al [58] proposed a centrally constrained adaptive A* algorithm that assigns dynamic weights to nodes at different positions and incorporates adaptive thresholds into the heuristic function to enhance adaptability.…”

Section: Graph Search Algorithmsmentioning

confidence: 99%

Path Planning Technique for Mobile Robots: A Review

Yang,

Li,

Qian

et al. 2023

Machines

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Mobile robot path planning involves designing optimal routes from starting points to destinations within specific environmental conditions. Even though there are well-established autonomous navigation solutions, it is worth noting that comprehensive, systematically differentiated examinations of the critical technologies underpinning both single-robot and multi-robot path planning are notably scarce. These technologies encompass aspects such as environmental modeling, criteria for evaluating path quality, the techniques employed in path planning and so on. This paper presents a thorough exploration of techniques within the realm of mobile robot path planning. Initially, we provide an overview of eight diverse methods for mapping, each mirroring the varying levels of abstraction that robots employ to interpret their surroundings. Furthermore, we furnish open-source map datasets suited for both Single-Agent Path Planning (SAPF) and Multi-Agent Path Planning (MAPF) scenarios, accompanied by an analysis of prevalent evaluation metrics for path planning. Subsequently, focusing on the distinctive features of SAPF algorithms, we categorize them into three classes: classical algorithms, intelligent optimization algorithms, and artificial intelligence algorithms. Within the classical algorithms category, we introduce graph search algorithms, random sampling algorithms, and potential field algorithms. In the intelligent optimization algorithms domain, we introduce ant colony optimization, particle swarm optimization, and genetic algorithms. Within the domain of artificial intelligence algorithms, we discuss neural network algorithms and fuzzy logic algorithms. Following this, we delve into the different approaches to MAPF planning, examining centralized planning which emphasizes decoupling conflicts, and distributed planning which prioritizes task execution. Based on these categorizations, we comprehensively compare the characteristics and applicability of both SAPF and MAPF algorithms, while highlighting the challenges that this field is currently grappling with.

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