Cooperative Multi-Robot Path Planning by Heuristic Priority Adjustment

Regele, Ralf; Levi, Paul

doi:10.1109/iros.2006.282480

Cited by 58 publications

(27 citation statements)

References 7 publications

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“…Velocity planners fix the paths that will be followed by each robot, then find a velocity schedule along those paths that avoids collisions [5,29,30,31,32,33,34]. Priority planners assign a priority to each robot, then plan for individual robots in decreasing order of priority, treating higher priority robots as moving obstacles [4,35,36,37,38,39]. The choice of priority ordering is critical, leading to a number of heuristics for choosing priority orders that are likely to lead to a solution [38,40,41].…”

Section: Decoupled Multirobot Path Planningmentioning

confidence: 99%

Subdimensional expansion for multirobot path planning

Wagner

Choset

2015

Artificial Intelligence

283

237

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Planning optimal paths for large numbers of robots is computationally expensive. In this paper, we introduce a new framework for multirobot path planning called subdimensional expansion, which initially plans for each robot individually, and then coordinates motion among the robots as needed. More specifically, subdimensional expansion initially creates a one-dimensional search space embedded in the joint configuration space of the multirobot system. When the search space is found to be blocked during planning by a robotrobot collision, the dimensionality of the search space is locally increased to ensure that an alternative path can be found. As a result, robots are only coordinated when necessary, which reduces the computational cost of finding a path. We present the M* algorithm, an implementation of subdimensional expansion that adapts the A* planner to perform efficient multirobot planning. M* is proven to be complete and to find minimal cost paths. Simulation results are presented that show that M* outperforms existing optimal multirobot path planning algorithms.

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Section: Decoupled Multirobot Path Planningmentioning

confidence: 99%

Subdimensional expansion for multirobot path planning

Wagner

Choset

2015

Artificial Intelligence

283

237

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“…As with when Ri's PMDline is facing the OWRpointi, the objective for Ri in moving closely to the OWRpointi can be fulfilled by the motion vector (Sxi(k+1), Syi(k+1)) T . Thus, AM(k) is designed as (11) in this case:.…”

Section: Attractive Momentsmentioning

confidence: 99%

“…In [10], [11], prioritized planning is discussed, which works as follows. First, priorities are assigned to the robots.…”

Section: Introductionmentioning

confidence: 99%

A Decentralized Method Using Artificial Moments for Multi-Robot Path-Planning

Chen

Zhao

et al. 2013

International Journal of Advanced Robotic Systems

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For the local path-planning of multi-robots, a decentralized method is presented where each robot plans its own path in the following steps for each iteration. Firstly, an optimal way representative point (OWRpoint) is obtained for guiding the robot to move along a shorter path. Then, the robot moves a step under the control of its own motion controller, which is designed based on artificial moments. In the motion controller, attractive and repulsive moments are used to move robots closer to their OWRpoints and away from obstacles, while coordinated moments are used to resolve the conflicts between robots. Two simulations are given to test the method and the results indicate that the method is valuable as it meets the requirements of the real-time property while optimizing the performance measure of each robot: namely, the path travelled to reach the robot's target.

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“…Reinforcement learning has been tested in many simulated environments [3] - [11] but on a limited basis in real-world scenarios. A real-world environment poses more challenges than a simulated environment, such as enlarged state spaces [2], increased computational complexity, significant safety issues (a real robot can cause real damage), and longer turnaround times for results.…”

Section: Introductionmentioning

confidence: 99%

A Deterministic Improved Q-Learning for Path Planning of a Mobile Robot

Konar

Chakraborty

Singh

et al. 2013

IEEE Trans. Syst. Man Cybern, Syst.

202

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Abstract-The paper provides a new deterministic Q-learning with a presumed knowledge about the distance from the current state to both the next state and the goal. This knowledge is efficiently used to update the entries in the Q-table once only by utilizing four derived properties of the Q-learning, instead of repeatedly updating them like the classical Q-learning. Naturally, the proposed algorithm has an insignificantly small timecomplexity in comparison to its classical counterpart. Further, the proposed algorithm stores the Q-value for the best possible action at a state, and thus saves significant storage. Experiments undertaken on simulated maze and real platforms confirm that the Q-table obtained by the proposed Q-learning when used for path-planning application of mobile robots outperforms both the classical and extended Q-learning with respect to three metrics: traversal time, number of states traversed, and 90 o turns required. Reduction in 90 o turnings minimizes the energy consumption, and thus has importance in robotics literature.

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Cooperative Multi-Robot Path Planning by Heuristic Priority Adjustment

Cited by 58 publications

References 7 publications

Subdimensional expansion for multirobot path planning

Subdimensional expansion for multirobot path planning

A Decentralized Method Using Artificial Moments for Multi-Robot Path-Planning

A Deterministic Improved Q-Learning for Path Planning of a Mobile Robot

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