Incremental multi-agent path finding

Semiz, Fatih; Polat, Faruk

doi:10.1016/j.future.2020.09.032

Cited by 9 publications

(4 citation statements)

References 12 publications

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“…11. Comparison method A is based on reference [13] and reproduces a state-of-the-art method that accounts for changes in the environment, which we have updated to fit the simulation environment. Therefore, it cannot plan the environment changes associated with task execution in advance but can respond by re-planning as changes occur.…”

Section: ) Methodsmentioning

confidence: 99%

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Multi-Agent Action Graph Based Task Allocation and Path Planning Considering Changes in Environment

Okubo

Takahashi

2023

IEEE Access

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Task allocation and path planning considering changes in the mobility of robots in the environment allows the robots to efficiently execute tasks with smaller travel times. A lunar base construction is one of the situations in which robots can more efficiently accomplish its goal by taking such environment changes into account when performing tasks. For the construction, we assumed that when a robot executes a task of building a road, the environment changes such that aisles that were unusable before the task become usable post execution. If such changes in environment are considered in advance, the robot can efficiently plan to wait until the environment changes and can move before executing the task. However, previous studies have not considered such changes, resulting in inefficient planning. To solve this problem, we developed a multi-agent action graph that consists of multiple layers and expresses the environment changes associated with task execution in terms of changes in these layers. In this graph, task allocation and path planning are formulated as a combinatorial optimization problem and are optimized using mixed-integer programming. Multi-agent action graphs and the proposed formulation enable efficient planning considering changes in the robots' mobility in advance. Through simulations, we confirmed that the proposed method completed the construction of the lunar base approximately 16.4% earlier than the conventional method, while consuming approximately 16.0% less total energy of the robots.INDEX TERMS Task allocation, path planning, environment changes, multi-robot systems, robotic lunar surface operations.

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Section: ) Methodsmentioning

confidence: 99%

“…Semiz et al [13] proposed a multi-agent path search that treats environment changes as changes in the available nodes of the environment represented as a graph. The environment change treated here is the closest to the definition this study dealt with, in that the available nodes change.…”

Section: A Studies That Consider Environment Changesmentioning

confidence: 99%

Multi-Agent Action Graph Based Task Allocation and Path Planning Considering Changes in Environment

Okubo

Takahashi

2023

IEEE Access

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“…To address the problem of traditional deep learning algorithms requiring long development time, Gao Jun li et al proposed a phased cultivation method PRM- TD3 to shorten the development time, which consists of PRM undergoing global planning TD3 and conducting local training for better flexibility in single-step time but performing poorly in overall path time [ 23 ]. In a multi-robot cooperative task, Semiz, Fatih et al performed initial planning based on the conflict search and used D* for the underlying planning of individual robots to improve resilience in dynamic scenarios [ 24 ]. D* has been used to determine the path cost node, and PSO has been used to optimize the control execution trajectory at the execution level [ 25 ].…”

Section: Related Workmentioning

confidence: 99%

PRM-D* Method for Mobile Robot Path Planning

Liu

Xie

Sui

et al. 2023

Sensors

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Various navigation tasks involving dynamic scenarios require mobile robots to meet the requirements of a high planning success rate, fast planning, dynamic obstacle avoidance, and shortest path. PRM (probabilistic roadmap method), as one of the classical path planning methods, is characterized by simple principles, probabilistic completeness, fast planning speed, and the formation of asymptotically optimal paths, but has poor performance in dynamic obstacle avoidance. In this study, we use the idea of hierarchical planning to improve the dynamic obstacle avoidance performance of PRM by introducing D* into the network construction and planning process of PRM. To demonstrate the feasibility of the proposed method, we conducted simulation experiments using the proposed PRM-D* (probabilistic roadmap method and D*) method for maps of different complexity and compared the results with those obtained by classical methods such as SPARS2 (improving sparse roadmap spanners). The experiments demonstrate that our method is non-optimal in terms of path length but second only to graph search methods; it outperforms other methods in static planning, with an average planning time of less than 1 s, and in terms of the dynamic planning speed, our method is two orders of magnitude faster than the SPARS2 method, with a single dynamic planning time of less than 0.02 s. Finally, we deployed the proposed PRM-D* algorithm on a real vehicle for experimental validation. The experimental results show that the proposed method was able to perform the navigation task in a real-world scenario.

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“…Based on the CBS algorithm, many improved methods have been proposed. Fatih Semiz [23] proposed an incremental algorithm by replacing the low-level A* algorithm of CBS with D*-lite for the multi-agent path planning problem in a dynamic environment. Bare [24] proposed an enhanced CBS (ECBS) algorithm that replaces the best-first search algorithm in the high and low levels of CBS with a focused search.…”

Section: Introductionmentioning

confidence: 99%

Multi-Conflict-Based Optimal Algorithm for Multi-UAV Cooperative Path Planning

Liu

Wang

et al. 2023

Drones

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Multi-UAV cooperative path planning can improve the efficiency of task completion. To deal with the space and time conflicts of multi-UAVs in complex environments, a multi-collision-based multi-UAV cooperative path planning algorithm, multi-conflict-based search (MCBS), is proposed. First, the flight and cooperative constraints of UAV are analyzed, and a three-dimensional environment model is established that incorporates geographical information. Then, hierarchical optimization is used to design collaborative algorithms. In the low-level path design, UAV flight constraints are combined with a sparse A* algorithm, and by improving the cost function, the search space is reduced, and the search time is shortened. In high-level cooperation, the priorities of different conflicts are set, heuristic information is introduced to guide the constraint tree to grow in the direction of satisfying the constraints, and the optimal path set is searched by the best priority search algorithm to reduce the convergence time. Finally, the planning results of the proposed algorithm, the traditional CBS algorithm, and the sparse A* algorithm for different UAV tasks are compared, and the influence of the optimization parameters on the calculation results is discussed. The simulation results show that the proposed algorithm can solve cooperative conflict between UAVs, improve the efficiency of path searches, and quickly find the optimal safe cooperative path that satisfies flight and cooperative constraints.

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Incremental multi-agent path finding

Cited by 9 publications

References 12 publications

Multi-Agent Action Graph Based Task Allocation and Path Planning Considering Changes in Environment

Multi-Agent Action Graph Based Task Allocation and Path Planning Considering Changes in Environment

PRM-D* Method for Mobile Robot Path Planning

Multi-Conflict-Based Optimal Algorithm for Multi-UAV Cooperative Path Planning

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