Abstract
This paper proposes a distributed estimation and control algorithm that enables a team of mobile robots to search for and track an unknown number of targets. These targets may be stationary or moving, and the number of targets may vary over time as targets enter and leave the area of interest. The robots are equipped with sensors that have a finite field of view and may experience false negative and false positive detections. The robots use a novel, distributed formulation of the Probability Hypothesis Density (PHD) filter, which accounts for the limitations of the sensors, to estimate the number of targets and the positions of the targets. The robots then use Lloyd’s algorithm, a distributed control algorithm that has been shown to be effective for coverage and search tasks, to drive their motion within the environment. We utilize the output of the PHD filter as the importance weighting function within Lloyd’s algorithm. This causes the robots to be drawn towards areas that are likely to contain targets. We demonstrate the efficacy of our proposed algorithm, including comparisons to a coverage-based controller with a uniform importance weighting function, through an extensive series of simulated experiments. These experiments show teams of 10–100 robots successfully tracking 10–50 targets in both 2D and 3D environments.
Highlights
Target search and tracking is a canonical task in robotics, encompassing problems such as mapping, surveillance, and search and rescue
The Probability Hypothesis Density (PHD) filter tracks the first moment of the distribution over random finite sets (RFSs), recursively updating the PHD using models of target motion and the measurement sets collected by the robots
There are two main components: (1) a novel, distributed PHD filter implementation and (2) a Voronoi-based control strategy. This combination of the PHD filter with Voronoi-based control is another contribution of our work
Summary
Target search and tracking is a canonical task in robotics, encompassing problems such as mapping, surveillance, and search and rescue. Pimenta et al (2009) present a decentralized approach for Simultaneous Coverage and Tracking (SCAT) They use the continuous time variant of Lloyd’s algorithm to create a control law with guaranteed exponential convergence to a local minimum of the objective function. We use the PHD as the importance weighting function in Lloyd’s algorithm, a new combination This naturally and effectively drives the robots to follow previously detected targets and to explore unknown areas that may contain new targets. The biggest contribution is an entirely new set of experiments that extend the previously 2D scenario to a 3D one in which a team of multirotor UAVs tracks a collection of targets moving on the ground These experiments demonstrate the flexibility of the proposed distributed estimation and control algorithms to work in a variety of settings, including with some uncertainty in the poses of the sensors. Note that this set encodes both the number of targets (i.e., the cardinality of the set |X t |) and the state of each target (i.e., the elements of the set xit )
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
