Efficient Multi-robot Search for a Moving Target

@article{Hollinger2009EfficientMS,
  title={Efficient Multi-robot Search for a Moving Target},
  author={Geoffrey A. Hollinger and Sanjiv Singh and Joseph Djugash and Athanasios Kehagias},
  journal={The International Journal of Robotics Research},
  year={2009},
  volume={28},
  pages={201 - 219}
}
This paper examines the problem of locating a mobile, non-adversarial target in an indoor environment using multiple robotic searchers. One way to formulate this problem is to assume a known environment and choose searcher paths most likely to intersect with the path taken by the target. We refer to this as the multi-robot efficient search path planning (MESPP) problem. Such path planning problems are NP-hard, and optimal solutions typically scale exponentially in the number of searchers. We… 

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References

SHOWING 1-10 OF 38 REFERENCES

Proofs and Experiments in Scalable, Near-Optimal Search by Multiple Robots

This paper presents a finite-horizon path enumeration algorithm for solving the Multi-robot Efficient Search Path Planning problem that utilizes sequential allocation to achieve linear scalability in the number of searchers and shows that the linearly scalable MESPP algorithm generates searcher paths competitive with those generated by exponential algorithms.

Probabilistic Strategies for Pursuit in Cluttered Environments with Multiple Robots

This paper proposes a scalable algorithm using an entropy cost heuristic that searches possible movement paths to determine coordination strategies for the robotic pursuers and successfully reduces capture time with limited pursuers in an environment beyond the scope of many other approaches.

A Visibility-Based Pursuit-Evasion Problem

This paper addresses the problem of planning the motion of one or more pursuers in a polygonal environment to eventually "see" an evader that is unpredictable, has unknown initial position, and is

Visibility-Based Pursuit-Evasion in a Polygonal Environment

This paper addresses the problem of planning the motion of one or more pursuers in a polygonal environment to eventually “see” an evader that is unpredictable, has unknown initial position, and is

Coordinated Search in Cluttered Environments Using Range from Multiple Robots

Real-time methods for incorporating non-line-of-sight range measurements into a framework for finding a non-adversarial target in cluttered environments using multiple robotic searchers and two Bayesian methods for updating the expected location of a mobile target and integrating these updates into planning are described.

Efficient Planning of Informative Paths for Multiple Robots

This paper presents an efficient path planning algorithm that coordinates multiple robots, each having a resource constraint, to maximize the "informativeness" of their visited locations, using a Gaussian Process to model the underlying phenomenon.

Probabilistic Search for a Moving Target in an Indoor Environment

A technique to obtain the upper bound of detection for solving the problem in a branch and bound framework is proposed and comparisons show that the technique is also superior to known bounding methods for the original optimal searcher path problem.

Probabilistic planning for robotic exploration

Planning algorithms that generate robot control policies for partially observable Markov decision process (POMDP) planning problems and the relevance of onboard science data analysis and POMDP planning to robotic exploration are demonstrated.

Multi-objective exploration and search for autonomous rescue robots: Research Articles

This paper presents a prototype system, featuring a strategic level, which can be used to adapt the task of exploration and search to specific rescue missions, and makes use of high-level representation of the robot plans through a Petri Net formalism.

Multi‐objective exploration and search for autonomous rescue robots

This paper presents a prototype system, featuring a strategic level, which can be used to adapt the task of exploration and search to specific rescue missions, and makes use of high‐level representation of the robot plans through a Petri Net formalism.