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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. 

This paper proposes a novel neural network-based control policy to enable a mobile robot to navigate safety through environments filled with both static obstacles, such as tables and chairs, and dense crowds of pedestrians. The network architecture uses early fusion to combine a short history of lidar data with kinematic data about nearby pedestrians. This kinematic data is key to enable safe robot navigation in these uncontrolled, human-filled environments. The network is trained in a supervised setting, using expert demonstrations to learn safe navigation behaviors. A series of experiments in detailed simulated environments demonstrate the efficacy of this policy, which is able to achieve a higher success rate than either standard model-based planners or state-of-the-art neural network control policies that use only raw sensor data. 

This paper introduces the normalized unused sensing capacity to measure the amount of information that a sensor is currently gathering relative to its theoretical maximum. This quantity can be computed using entirely local information and works for arbitrary sensor models, unlike previous literature on the subject. This is then used to develop a distributed coverage control strategy for a team of heterogeneous sensors that automatically balances the load based on the current unused capacity of each team member. This algorithm is validated in a multi-target tracking scenario, yielding superior results to standard approaches that do not account for heterogeneity or current usage rates. 

Accurately tracking dynamic targets relies on robots accounting for uncertainties in their own states to share information and maintain safety. The problem becomes even more challenging when there are an unknown and time-varying number of targets in the environment. In this paper we address this problem by introducing four new distributed algorithms that allow large teams of robots to: i) run the prediction and ii) update steps of a distributed recursive Bayesian multitarget tracker, iii) determine the set of local neighbors that must exchange data, and iv) exchange data in a consistent manner. All of these algorithms account for a bounded level of localization uncertainty in the robots by leveraging our recent introduction of the convex uncertainty Voronoi (CUV) diagram, which extends the traditional Voronoi diagram to account for localization uncertainty. The CUV diagram introduces a tessellation over the environment, which we use in this work both to distribute the multi-target tracker and to make control decisions about where to search next. We examine the efficacy of our method via a series of simulations and compare them to our previous work which assumed perfect localization. 

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 

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. 

In order for a mobile robot to be able to effectively operate in complex, dynamic environments it must be capable of understanding both where and what the objects around them are. In this paper we introduce the semantic probability hypothesis density (SPHD) filter, which allows robots to simultaneously track multiple classes of targets despite measurement uncertainty, including false positive detections, false negative detections, measurement noise, and target misclassification. The SPHD filter is capable of incorporating a different motion model for each type of target and of functioning in situations where the number of targets is unknown and time-varying. We demonstrate the efficacy of the SPHD filter via simulations with multiple target types containing both static and dynamic targets. We show that the SPHD filter performs better than a collection of PHD filters running in parallel, one for each target class.