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1. Decentralized Multi-Agent Search for Moving Targets Using Road Network Gaussian Process Regressions

   https://doi.org/10.3390/drones8110606  

   Moon, Brady; Akagi, Christine; Peterson, Cameron K (November 2024, Drones)

   Unmanned aerial vehicles (UAVs) can collaborate as teams to accomplish diverse mission objectives, such as target search and tracking. This paper introduces a method that leverages accumulated target-density information over the course of a UAV mission to adapt path-planning rewards, guiding UAVs toward areas with a higher likelihood of target presence. The target density is modeled using a Gaussian process, which is iteratively updated as the UAVs search the environment. Unlike conventional search algorithms that prioritize unexplored regions, this approach incentivizes revisiting target-rich areas. The target-density information is shared across UAVs using decentralized consensus filters, enabling cooperative path selection that balances the exploration of uncertain regions with the exploitation of known high-density areas. The framework presented in this paper provides an adaptive cooperative search method that can quickly develop an understanding of the region’s target-dense areas, helping UAVs refine their search. Through Monte Carlo simulations, we demonstrate this method in both a 2D grid region and road networks, showing up to a 26% improvement in target density estimates. 

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2. Comparing Stochastic Optimization Methods for Multi-robot, Multi-target Tracking

   https://doi.org/10.1007/978-3-031-51497-5_27  

   Xin, Pujie; Dames, Philip (February 2024, Springer Nature Switzerland)

   This paper compares different distributed control approaches which enable a team of robots search for and track an unknown number of targets. The robots are equipped with sensors which have a limited field of view (FoV) and they are required to explore the environment. The team uses a distributed formulation of the Probability Hypothesis Density (PHD) filter to estimate the number and the position of the targets. The resulting target estimate is used to select the subsequent search locations for each robot. This paper compares Lloyd’s algorithm, a traditional method for distributed search, with two typical stochastic optimization methods: Particle Swarm Optimization (PSO) and Simulated Annealing (SA). This paper presents novel formulations of PSO and SA to solve the multi-target tracking problem, which more effectively trade off between exploration and exploitation. Simulations demonstrate that the use of these stochastic optimization techniques improves coverage of the search space and reduces the error in the target estimates compared to the baseline approach. 

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3. Distributed Multi-robot Tracking of Unknown Clustered Targets with Noisy Measurements

   https://doi.org/10.1007/978-3-031-51497-5_10  

   Chen, Jun; Dames, Philip; Park, Shinkyu (January 2024, Springer Nature Switzerland)

   Distributed multi-target tracking is a canonical task for multi-robot systems, encompassing applications from environmental monitoring to disaster response to surveillance. In many situations, the distribution of unknown objects in a search area is irregular, with objects are likely to distribute in clusters instead of evenly distributed. In this paper, we develop a novel distributed multi-robot multi-target tracking algorithm for effectively tracking clustered targets from noisy measurements. Our algorithm contains two major components. Firstly, both the instantaneous and cumulative target density are estimated, providing the best guess of current target states and long-term coarse distribution of clusters, respectively. Secondly, the power diagram is implemented in Lloyd’s algorithm to optimize task space assignment for each robot to trade-off between tracking detected targets in clusters and searching for potential targets outside clusters. We demonstrate the efficacy of our proposed method and show that our method outperforms of other candidates in tracking accuracy through a set of simulations. 

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4. Distributed Multiple Hypothesis Tracker for Mobile Sensor Networks

   https://doi.org/10.1007/978-3-031-51497-5_22  

   Xin, Pujie; Dames, Philip (February 2024, Springer Nature Switzerland)

   This paper proposes a distributed estimation and control algorithm to allow a team of robots to search for and track an unknown number of targets. The number of targets in the area of interest varies over time as targets enter or leave, and there are many sources of sensing uncertainty, including false positive detections, false negative detections, and measurement noise. The robots use a novel distributed Multiple Hypothesis Tracker (MHT) to estimate both the number of targets and the states of each target. A key contribution is a new data association method that reallocates target tracks across the team. The distributed MHT is compared against another distributed multi-target tracker to test its utility for multi-robot, multi-target tracking. 

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5. Conflict-Based Steiner Search for Multi-Agent Combinatorial Path Finding.

   Ren, Zhongqiang; Rathinam, Sivakumar; Choset, Howie (January 2022, Proceedings of Robotics: Science and Systems)

   NA (Ed.)

   Conventional Multi-Agent Path Finding (MAPF) problems aim to compute an ensemble of collision-free paths for multiple agents from their respective starting locations to pre-allocated destinations. This work considers a generalized version of MAPF called Multi-Agent Combinatorial Path Finding (MCPF) where agents must collectively visit a large number of intermediate target locations along their paths before arriving at destinations. This problem involves not only planning collisionfree paths for multiple agents but also assigning targets and specifying the visiting order for each agent (i.e. multi-target sequencing). To solve the problem, we leverage the well-known Conflict-Based Search (CBS) for MAPF and propose a novel framework called Conflict-Based Steiner Search (CBSS). CBSS interleaves (1) the conflict resolving strategy in CBS to bypass the curse of dimensionality in MAPF and (2) multiple traveling salesman algorithms to handle the combinatorics in multi-target sequencing, to compute optimal or bounded sub-optimal paths for agents while visiting all the targets. Our extensive tests verify the advantage of CBSS over baseline approaches in terms of computing shorter paths and improving success rates within a runtime limit for up to 20 agents and 50 targets. We also evaluate CBSS with several MCPF variants, which demonstrates the generality of our problem formulation and the CBSS framework. 

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