Multi-robot path planning based on improved D* Lite Algorithm

Peng, Jung-Hao; Li, I-Hsum; Chien, Yi-Hsing; Hsu, Chen-Chien; Wang, Wei-Yen

doi:10.1109/icnsc.2015.7116061

Cited by 19 publications

(12 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The A * algorithm is the most efficient direct search solution for the shortest paths in a static road network and is a common heuristic for many other problems. The algorithm can be further understood by the formula f(n) = g(n)+h(n), where the left side of equation f(n) represents the cost estimate of the object from the initial state through state n to the target state, the right side of equation g(n) is the actual cost from the initial state to state n in the state space, and h(n) is the estimated cost of the best path from state n to the target state [72]. Choosing the evaluation function f(n) in the A * algorithm is extremely important, and the choice of the evaluation function is related to the planning of the shortest and best path of the AUV [68].…”

Section: ) Dijkstra's Algorithmmentioning

confidence: 99%

“…The D * Lite algorithm is based on the Dijkstra algorithm and is oriented to the algorithm of the optimal path search problem, with the starting point changing with time and a fixed target point [72]. This algorithm is simpler than the Dijkstra algorithm, and its planning; therefore, its suitability for an underwater dynamic path search is proven in [76] and [77].…”

Section: ) D * Lite Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Path Planning Technologies for Autonomous Underwater Vehicles-A Review

Li¹,

Wang²,

Du³

2019

IEEE Access

131

View full text Add to dashboard Cite

An autonomous underwater vehicle (AUV) is an economical and safe tool that is well-suited for search, investigation, identification, and salvage operations on the sea floor. Path planning technology, which primarily includes modeling methods and path search algorithms, is an important technology for AUVs. In recent years, the AUV path planning technology has rapidly developed. Compared with land robots, AUVs must endure complex underwater environments and consider various factors, such as currents, water pressure, and topography. Challenges exist in terms of online obstacle avoidance, three-dimensional environment path planning, and the robustness of the algorithms. Adapting a complex environment and finding a suitable path planning method comprise the main problem that must be solved. In this paper, we summarize the principles, advantages, and disadvantages of modeling and path search technologies for AUVs. The most prominent feature of this paper is to summarize the improvement methods of various technical shortcomings and improve the original methods, such as dynamic obstacle avoidance, optimization path, coverage, and processing speed. In addition to summarizing the characteristics of each algorithm, this paper intuitively demonstrates the experimental environment, the real-time nature, the path planning range of the AUV, and so on. We also discuss the application scenarios of various modeling and path search technologies for AUVs. In addition, we discuss the challenges of AUVs and the direction of future research. INDEX TERMS AUV, path planning, model building, path search.

show abstract

Section: ) Dijkstra's Algorithmmentioning

confidence: 99%

Section: ) D * Lite Algorithmmentioning

confidence: 99%

Path Planning Technologies for Autonomous Underwater Vehicles-A Review

Li¹,

Wang²,

Du³

2019

IEEE Access

131

View full text Add to dashboard Cite

show abstract

“…Once a map is constructed, there are multiple ways to plan routes within that map. Some methods for planning include A* [44, 45], D* [46–48], probabilistic roadmap (PRM) [49, 50], and rapidly‐exploring random tree (RRT) [51]. While the A* and D* algorithms find the lowest‐cost path, they have high computational requirements which may hinder path‐planning performance.…”

Section: Gps‐denied Navigation and Localisationmentioning

confidence: 99%

Survey of unmanned subterranean exploration, navigation, and localisation

Martz

Al-Sabban

Smith

2020

IET cyber-systems robotics

View full text Add to dashboard Cite

Subsurface networks include mining tunnels, caves, and the urban underground. Each of these environments presents a complex setting with significant challenges in exploration, exploitation, etc. Multiple hazards exist, including environmental and structural, and conditions degrade and change temporally. We present a survey of research in autonomy, networking and mobility focused on exploring and/or mapping subsurface networks in unpredictable and unexplored environments. The focus is on mining tunnels as a proxy subterranean environment; motivated by the proximity of the authors to the Bonita Peak Mining District; a Superfund site consisting of 48 historic mines which have contaminated soil, groundwater and surface water with heavy metals as a result of historic practices. The exploration and assessment of the mining tunnels for remediation efforts presents an extremely challenging problem in subterranean navigation. Arguably, the environment in question is the most extreme and challenging subterranean environment. Unmanned assets must enter to explore and re‐map the tunnels to assess safety for subsequent entrance by robots and/or humans for proper remediation. The survey is split into three categories — Locomotion, GPS‐denied navigation and localization, and Communication. It is concluded with a proposed design for a platform that addresses the difficulties of exploring an abandoned mine

show abstract

“…Several robots have used the D* algorithm in combination with the Morphin local planner (which version provides local navigational autonomy for the NASA Mars Exploration Rovers [22]) to drive autonomously on rough terrain over long distances [23][24][25]. The D* algorithm is also successfully used as a planner for multi-robot systems [26], as well as D* Lite [27,28].…”

Section: Related Workmentioning

confidence: 99%

Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment with Multi-Criteria Decision Making Using Full Consistency Method

2019

View full text Add to dashboard Cite

The progress in the research of various areas of robotics, artificial intelligence, and other similar scientific disciplines enabled the application of multi-robot systems in different complex environments and situations. It is necessary to elaborate the strategies regarding the path planning and paths coordination well in order to efficiently execute a global mission in common environment, prior to everything. This paper considers the multi-robot system based on the cloud technology with a high level of autonomy, which is intended for execution of tasks in a complex and crowded environment. Cloud approach shifts computation load from agents to the cloud and provides powerful processing capabilities to the multi-robot system. The proposed concept uses a multi-robot path planning algorithm that can operate in an environment that is unknown in advance. With the aim of improving the efficiency of path planning, the implementation of Multi-criteria decision making (MCDM) while using Full consistency method (FUCOM) is proposed. FUCOM guarantees the consistent determination of the weights of factors affecting the robots motion to be symmetric or asymmetric, with respect to the mission specificity that requires the management of multiple risks arising from different sources, optimizing the global cost map in that way.

show abstract

Multi-robot path planning based on improved D* Lite Algorithm

Cited by 19 publications

References 10 publications

Path Planning Technologies for Autonomous Underwater Vehicles-A Review

Path Planning Technologies for Autonomous Underwater Vehicles-A Review

Survey of unmanned subterranean exploration, navigation, and localisation

Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment with Multi-Criteria Decision Making Using Full Consistency Method

Contact Info

Product

Resources

About