skip to main content


This content will become publicly available on December 1, 2024

Title: The Convex Uncertain Voronoi Diagram for Safe Multi-Robot Multi-Target Tracking Under Localization Uncertainty
Accurately detecting, localizing, and tracking an unknown and time-varying number of dynamic targets using a team of mobile robots is a challenging problem that requires robots to reason about the uncertainties in their collected measurements. The problem is made more challenging when robots are uncertain about their own states, as this makes it difficult to both collectively localize targets and avoid collisions with one another. In this paper, we introduce the convex uncertain Voronoi (CUV) diagram, a generalization of the standard Voronoi diagram that accounts for the uncertain pose of each individual robot. We then use the CUV diagram to develop distributed multi-target tracking and coverage control algorithms that enable teams of mobile robots to account for bounded uncertainty in the location of each robot. Our algorithms are capable of safely driving mobile robots towards areas of high information distribution while maintaining coverage of the whole area of interest. We demonstrate the efficacy of these algorithms via a series of simulated and hardware tests, and compare the results to our previous work which assumes perfect localization.  more » « less
Award ID(s):
1830419
NSF-PAR ID:
10510591
Author(s) / Creator(s):
;
Publisher / Repository:
Springer
Date Published:
Journal Name:
Journal of Intelligent & Robotic Systems
Volume:
109
Issue:
4
ISSN:
0921-0296
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. In this paper, we propose a distributed coverage control algorithm for mobile sensing networks that can account for bounded uncertainty in the location of each sensor. Our algorithm is capable of safely driving mobile sensors towards areas of high information distribution while having them maintain coverage of the whole area of interest. To do this, we propose two novel variants of the Voronoi diagram. The first, the convex uncertain Voronoi (CUV) diagram, guarantees full coverage of the search area. The second, collision avoidance regions (CARs), guarantee collision-free motions while avoiding deadlock, enabling sensors to safely and successfully reach their goals. We demonstrate the efficacy of these algorithms via a series of simulations with different numbers of sensors and uncertainties in the sensors’ locations. The results show that sensor networks of different scales are able to safely perform optimized distribution corresponding to the information distribution density under different localization uncertainties 
    more » « less
  2. This paper addresses the complete area coverage problem of a known environment by multiple-robots. Complete area coverage is the problem of moving an end-effector over all available space while avoiding existing obstacles. In such tasks, using multiple robots can increase the efficiency of the area coverage in terms of minimizing the operational time and increase the robustness in the face of robot attrition. Unfortunately, the problem of finding an optimal solution for such an area coverage problem with multiple robots is known to be NP-complete. In this paper we present two approximation heuristics for solving the multi-robot coverage problem. The first solution presented is a direct extension of an efficient single robot area coverage algorithm, based on an exact cellular decomposition. The second algorithm is a greedy approach that divides the area into equal regions and applies an efficient single-robot coverage algorithm to each region. We present experimental results for two algorithms. Results indicate that our approaches provide good coverage distribution between robots and minimize the workload per robot, meanwhile ensuring complete coverage of the area. 
    more » « less
  3. We study two multi-robot assignment problems for multi-target tracking. We consider distributed approaches in order to deal with limited sensing and communication ranges. We seek to simultaneously assign trajectories and targets to the robots. Our focus is on \emph{local} algorithms that achieve performance close to the optimal algorithms with limited communication. We show how to use a local algorithm that guarantees a bounded approximate solution within $\mathcal{O}(h\log{1/\epsilon})$ communication rounds. We compare with a greedy approach that achieves a $2$--approximation in as many rounds as the number of robots. Simulation results show that the local algorithm is an effective solution to the assignment problem. 
    more » « less
  4. Presented at the Workshop on Heterogeneous Multi-Robot Task Allocation and Coordination. The authors recently developed a distributed algorithm to enable a team of homogeneous robots to search for and track an unknown and time-varying number of dynamic targets. This algorithm combined a distributed version of the PHD filter (for multi-target tracking) with Lloyd’s algorithm to drive the motion of the robots. In this paper we extend this previous work to allow a heterogeneous team of groundand aerial robots to perform the search and tracking tasks in a coordinated manner. Both types of robots are equipped with sensors that have a finite field of view and which may receive both false positive and false negative detections. Theaerial robots may vary the size of their sensor field of view (FoV) by changing elevation. This increase in the FoV coincides with a decrease in the accuracy and reliability of the sensor. The ground robots maintain the target tracking information while the aerial robots provide additional sensor coverage. We develop two new distributed algorithms to provide filter updates and to make control decisions in this heterogeneous team. Both algorithms only require robots to communicate with nearby robots and use minimal bandwidth.We demonstrate the efficacy of our approach through a series of simulated experiments which show that the heterogeneous teams are able to achieve more accurate tracking in less time than our previous work. 
    more » « less
  5. We consider the problem of multi-robot sensor coverage, which deals with deploying a multi-robot team in an environment and optimizing the sensing quality of the overall environment. As real-world environments involve a variety of sensory information, and individual robots are limited in their available number of sensors, successful multi-robot sensor coverage requires the deployment of robots in such a way that each individual team member’s sensing quality is maximized. Additionally, because individual robots have varying complements of sensors and both robots and sensors can fail, robots must be able to adapt and adjust how they value each sensing capability in order to obtain the most complete view of the environment, even through changes in team composition. We introduce a novel formulation for sensor coverage by multi-robot teams with heterogeneous sensing capabilities that maximizes each robot's sensing quality, balancing the varying sensing capabilities of individual robots based on the overall team composition. We propose a solution based on regularized optimization that uses sparsity-inducing terms to ensure a robot team focuses on all possible event types, and which we show is proven to converge to the optimal solution. Through extensive simulation, we show that our approach is able to effectively deploy a multi-robot team to maximize the sensing quality of an environment, responding to failures in the multi-robot team more robustly than non-adaptive approaches. 
    more » « less