On multiple moving objects

Erdmann, Michael; Lozano-Pérez, Tomás

doi:10.1109/robot.1986.1087401

Cited by 262 publications

(223 citation statements)

References 9 publications

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“…Prioritized planning techniques also avoid conflicts through sequential planning, where the planning order is determined by the priority assigned to each agent (Erdmann and Lozano-Pérez 1987). A search over different priority orders can be used to find an ordering that reduces the global cost (Azarm and Schmidt 1997).…”

Section: Decentralized Planningmentioning

confidence: 99%

Decentralized path planning for multi-agent teams with complex constraints

2012

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This paper presents a novel approach to address the challenge of planning paths for multi-agent systems subject to complex constraints. The technique, called the Decentralized Multi-Agent Rapidly-exploring Random Tree (DMA-RRT) algorithm, extends the Closed-loop RRT (CL-RRT) algorithm to handle multiple agents while retaining its ability to plan quickly. A core component of the DMA-RRT algorithm is a merit-based token passing coordination strategy that makes use of the tree of feasible trajectories grown in the CL-RRT algorithm to dynamically update the order in which agents replan. The reordering is based on a measure of each agent's incentive to change the plan and allows agents with a greater potential improvement to replan sooner, which is demonstrated to improve the team's overall performance compared to a traditional, scripted replan order. The main contribution of the work is a version of the algorithm, called Cooperative DMA-RRT, which introduces a cooperation strategy that allows an agent to modify its teammates' plans in order to select paths that reduce their combined cost. This modification further improves team performance and avoids certain common deadlock scenarios. The paths generated by both algorithms are proven to satisfy inter-agent constraints, such as collision avoidance, and numerous simulation and experimental results are presented to demonstrate their performance.

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Section: Decentralized Planningmentioning

confidence: 99%

Decentralized path planning for multi-agent teams with complex constraints

2012

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“…A variant of decoupled planning, called priori tizing planning, plans for one robot at a time, in some sequence, considering the robots whose trajec tories have already been planned as moving obstacles [7,10,13]. In ref.…”

Section: Multi-robot Motion Planningmentioning

confidence: 99%

Probabilistic Road Map sampling strategies for multi-robot motion planning

Clark

2005

Robotics and Autonomous Systems

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This paper presents a Probabilistic Road Map (PRM) motion planning algorithm to be queried within Dynamic Robot Networks-a multi-robot coordination platform for robots operating with limited sensing and inter-robot communication.First, the Dynamic Robot Networks (DRN) coordination platform is introduced that facilitates centralized robot coordination across ad hoc networks, allowing safe navigation in dynamic, unknown environments. As robots move about their environ ment, they dynamically form communication networks. Within these networks, robots can share local sensing information and coordinate the actions of all robots in the network.Second, a fast single-query Probabilistic Road Map (PRM) to be called within the DRN platform is presented that has been augmented with new sampling strategies. Traditional PRM strategies have shown success in searching large configuration spaces. Considered here is their application to on-line, centralized, multiple mobile robot planning problems. New sampling strategies that exploit the kinematics of non-holonomic mobile robots have been developed and implemented. First, an appropriate method of selecting milestones in a PRM is identified to enable fast coverage of the configuration space. Second, a new method of generating PRM milestones is described that decreases the planning time over traditional methods. Finally, a new endgame region for multi-robot PRMs is presented that increases the likelihood of finding solutions given difficult goal configurations.Combining the DRN platform with these new sampling strategies, on-line centralized multi-robot planning is enabled. This allows robots to navigate safely in environments that are both dynamic and unknown. Simulations and real robot experiments are presented that demonstrate: (1) speed improvements accomplished by the sampling strategies, (2) centralized robot coordination across Dynamic Robot Networks, (3) on-the-fly motion planning to avoid moving and previously unknown obstacles and (4) autonomous robot navigation towards individual goal locations.

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“…Methods often vary significantly based on the use of decoupled planning (planning the paths independently and then coordinating the robot trajectories [15], [19], [40], [84]) or centralized planning (planning occurs in a composite configuration space [3], [8], [102]). For example, the decoupled planning representations constructed in [69] and [91] can be generalized to a broad class of state spaces, including coordination along a configuration-space roadmap for each robot.…”

Section: Paradigmmentioning

confidence: 99%

Robot Motion Planning: A Game-Theoretic Foundation

LaValle¹

2000

Algorithmica

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Analysis techniques and algorithms for basic path planning have become quite valuable in a variety of applications such as robotics, virtual prototyping, computer graphics, and computational biology. Yet, basic path planning represents a very restricted version of general motion planning problems often encountered in robotics. Many problems can involve complications such as sensing and model uncertainties, nonholonomy, dynamics, multiple robots and goals, optimality criteria, unpredictability, and nonstationarity, in addition to standard geometric workspace constraints. This paper proposes a unified, game-theoretic mathematical foundation upon which analysis and algorithms can be developed for this broader class of problems, and is inspired by the similar benefits that were obtained by using unified configuration-space concepts for basic path planning. By taking this approach, a general algorithm has been obtained for computing approximate optimal solutions to a broad class of motion planning problems, including those involving uncertainty in sensing and control, environment uncertainties, and the coordination of multiple robots.

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On multiple moving objects

Cited by 262 publications

References 9 publications

Decentralized path planning for multi-agent teams with complex constraints

Decentralized path planning for multi-agent teams with complex constraints

Probabilistic Road Map sampling strategies for multi-robot motion planning

Robot Motion Planning: A Game-Theoretic Foundation

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