D* Lite Based Real-Time Multi-Agent Path Planning in Dynamic Environments

Al-Mutib, Khalid; Alsulaiman, Mansour; Emaduddin, Muhammad; Hedjar, Ramdane; Mattar, Ebrahim

doi:10.1109/cimsim.2011.38

Cited by 37 publications

(16 citation statements)

References 9 publications

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“…D* is an incremental algorithm widely used for path planning in a completely unknown or partially known environment, both for single robot and for multi-robot systems [6]. A multi-agent version of D* Lite, which is a variant of D* [18], is proposed in [1]. Another approach to apply D* Lite for planning the path for multi-robot system with implemented concept of parallel processing is proposed in [30].…”

Section: Related Workmentioning

confidence: 99%

Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment Using Fuzzy Logic and Online Learning

Zagradjanin

Rodić

Pamučar

et al. 2021

ITC

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This paper considers an autonomous cloud-based multi-robot system designed to execute highly repetitive tasksin a dynamic environment such as a modern megastore. Cloud level is intended for performing the most demandingoperations in order to unload the robots that are users of cloud services in this architecture. For path planningon global level D* Lite algorithm is applied, bearing in mind its high efficiency in dynamic environments. In orderto introduce smart cost map for further improvement of path planning in complex and crowded environment, implementationof fuzzy inference system and learning algorithm is proposed. The results indicate the possibility ofapplying a similar concept in different real-world robotics applications, in order to reduce the total paths length,as well as to minimize the risk in path planning related to the human-robot interactions.

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Section: Related Workmentioning

confidence: 99%

Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment Using Fuzzy Logic and Online Learning

Zagradjanin

Rodić

Pamučar

et al. 2021

ITC

View full text Add to dashboard Cite

show abstract

“…A* is an extension to Dijkstra algorithm, where it combines the cost search with heuristics that guide the search towards the goal point to achieve quicker searching performance. Many extensions of A* have been proposed, for instance Lifelong Planning A* (LPA*) [ 15 ] was developed to support changes in the environment without recalculating the entire graph, D* Lite [ 16 ] extends LPA* to re-plan the path while the robot is moving, Anytime Repairing A* (ARA*) [ 17 ] improves the optimality of the path by reusing suboptimal solutions from previous executions, and Hybrid-state A* [ 18 ] generates the graph based on the robot dynamics and thus searches for a dynamically feasible path.…”

Section: Related Workmentioning

confidence: 99%

Optimal Motion Planning in GPS-Denied Environments Using Nonlinear Model Predictive Horizon

Younes

Barczyk

2021

Sensors

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Navigating robotic systems autonomously through unknown, dynamic and GPS-denied environments is a challenging task. One requirement of this is a path planner which provides safe trajectories in real-world conditions such as nonlinear vehicle dynamics, real-time computation requirements, complex 3D environments, and moving obstacles. This paper presents a methodological motion planning approach which integrates a novel local path planning approach with a graph-based planner to enable an autonomous vehicle (here a drone) to navigate through GPS-denied subterranean environments. The local path planning approach is based on a recently proposed method by the authors called Nonlinear Model Predictive Horizon (NMPH). The NMPH formulation employs a copy of the plant dynamics model (here a nonlinear system model of the drone) plus a feedback linearization control law to generate feasible, optimal, smooth and collision-free paths while respecting the dynamics of the vehicle, supporting dynamic obstacles and operating in real time. This design is augmented with computationally efficient algorithms for global path planning and dynamic obstacle mapping and avoidance. The overall design is tested in several simulations and a preliminary real flight test in unexplored GPS-denied environments to demonstrate its capabilities and evaluate its performance.

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“…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

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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.

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D* Lite Based Real-Time Multi-Agent Path Planning in Dynamic Environments

Cited by 37 publications

References 9 publications

Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment Using Fuzzy Logic and Online Learning

Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment Using Fuzzy Logic and Online Learning

Optimal Motion Planning in GPS-Denied Environments Using Nonlinear Model Predictive Horizon

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

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