Multi-Robot Swarm for Cooperative Scalar Field Mapping

Multi-Robot Swarm for Cooperative Scalar Field Mapping

Hung Manh La (University of Nevada, USA)
DOI: 10.4018/978-1-4666-9572-6.ch014
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In this chapter, autonomous mobile robots are deployed to measure an unknown scalar field and build its map. The development of a cooperative sensing and control method is presented for multi-robot swarming to build the scalar field map. The proposed method consists of two parts. First, the development of a distributed sensor fusion algorithm is obtained by integrating two different distributed consensus filters to achieve cooperative sensing among robots. This fusion algorithm has two phases. In the first phase, the weighted average consensus filter is developed which allows each robot to find an estimate of the value of the scalar field. In the second phase, the average consensus filter is used to allow each robot to find a confidence of the estimate. The final estimate of the value of the scalar field is iteratively updated during the movement of the robot via a weighted average protocol. Second, the distributed control algorithm is developed to control the mobile robots to form a network and cover the field. Experimental results are provided to demonstrate the proposed algorithms.
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System Modeling

In this section we present the models of multi-robot system, scalar field, and sensor measurement of each robot.

Key Terms in this Chapter

Scalar Field: Represents an oil spill or chemical leak field.

Multi-Robot Collaboration: Multiple robots can work together to complete a task efficiently.

Mobile Robot Network: Multiple mobile robots move together to form a connected network for collaboration on the field.

Formation Control: The robots can move together without collision and form certain pattern/formation to perform the task better.

Cooperative Sensing: The robots collect their sensing measurements and exchange information with their neighboring robots to achieve the global goal.

Consensus Filter: The consensus filter is to allow the robots to find out an agreement on their estimations in a distributed manner.

Flocking Control: Multiple robots can move/flock together like a school of fish or bird flock.

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