Beipeng Mu

Efficient distributed sensing using adaptive censoring-based inference

In many distributed sensing applications it is likely that only a few agents will have valuable information at any given time. Since wireless communication between agents is resource-intensive, it is important to ensure that the communication effort is focused on communicating valuable information from informative agents. This paper presents communication-efficient distributed sensing algorithms that avoid network cluttering by having only agents with high Value of Information (VoI) broadcast their measurements to the network, while others censor themselves. A novel contribution of the presented distributed estimation algorithm is the use of an adaptively adjusted VoI threshold to determine which agents are informative. This adaptation enables the team to better balance between the communication cost incurred and the long-term accuracy of the estimation. Theoretical results are presented establishing the almost sure convergence of the communication cost and estimation error to zero for distributions in the exponential family. Furthermore, validation through real datasets shows that the new VoI-based algorithms can yield improved parameter estimates than those achieved by previously published hyperparameter consensus algorithms while incurring only a fraction of the communication cost.

Two-Stage Focused Inference for Resource-Constrained Collision-Free Navigation

United States. Army Research Office. Multidisciplinary University Research Initiative (Grant W911NF-11-1-0391)

SLAM with objects using a nonparametric pose graph

Mapping and self-localization in unknown environments are fundamental capabilities in many robotic applications. These tasks typically involve the identification of objects as unique features or landmarks, which requires the objects both to be detected and then assigned a unique identifier that can be maintained when viewed from different perspectives and in different images. The data association and simultaneous localization and mapping (SLAM) problems are, individually, well-studied in the literature. But these two problems are inherently tightly coupled, and that has not been well-addressed. Without accurate SLAM, possible data associations are combinatorial and become intractable easily. Without accurate data association, the error of SLAM algorithms diverge easily. This paper proposes a novel nonparametric pose graph that models data association and SLAM in a single framework. An algorithm is further introduced to alternate between inferring data association and performing SLAM. Experimental results show that our approach has the new capability of associating object detections and localizing objects at the same time, leading to significantly better performance on both the data association and SLAM problems than achieved by considering only one and ignoring imperfections in the other.

Value-of-information aware active task assignment

This paper discusses the problem of robust allocation of unmanned vehicles (T.N) to targets with uncertainties. In particular, the team consists of heterogeneous vehicles with different exploration and exploitation abilities. A general framework is presented to model uncertainties in the planning problems, which goes beyond traditional Gaussian noise. Traditionally, exploration and exploitation are decoupled into two assignment problems are planned with un-correlated goals. The coupled planning method considered here assign exploration vehicles based on its potential influence of the exploitation. Furthermore, a fully decentralized algorithm, Consensus-Based Bundle Algorithm (CBBA), is used to implement the decoupled and coupled methods. CBBA can handle system dynamic constraints such as target distance, vehicle velocities, and has computation complexity polynomial to the number of vehicles and targets. The coupled method is shown to have improved planning performance in a simulated scenario with uncertainties about target classification.

Information-based Active SLAM via topological feature graphs

Exploring an unknown space and building maps is a fundamental capability for mobile robots. For fully autonomous systems, the robot would further need to actively plan its paths during exploration. The problem of designing robot trajectories to actively explore an unknown environment and minimize the map error is referred to as active simultaneous localization and mapping (active SLAM). Existing work has focused on planning paths with occupancy grid maps, which do not scale well and suffer from long term drift. This work proposes a Topological Feature Graph (TFG) representation that scales well and develops an active SLAM algorithm with it. The TFG uses graphical models, which utilize independences between variables, and enables a unified quantification of exploration and exploitation gains with a single entropy metric. Hence, it facilitates a natural and principled balance between map exploration and refinement. A probabilistic roadmap path-planner is used to generate robot paths in real time. Experimental results demonstrate that the proposed approach achieves better accuracy than a standard grid-map based approach while requiring orders of magnitude less computation and memory resources.

Efficient distributed sensing using adaptive censoring based inference

In many distributed sensing applications it is likely that only a few agents will have valuable information at any given time. Since wireless communication between agents is resource-intensive, it is important to ensure that the communication effort is focused on communicating valuable information from informative agents. This paper presents communication-efficient distributed sensing algorithms that avoid network cluttering by having only agents with high Value of Information (VoI) broadcast their measurements to the network, while others censor themselves. A novel contribution of the presented distributed estimation algorithm is the use of an adaptively adjusted VoI threshold to determine which agents are informative. This adaptation enables the team to better balance between the communication cost incurred and the long-term accuracy of the estimation. Theoretical results are presented establishing the almost sure convergence of the communication cost and estimation error to zero for distributions in the exponential family. Furthermore, validation through real datasets shows that the new VoI-based algorithms can yield improved parameter estimates than those achieved by previously published hyperparameter consensus algorithms while incurring only a fraction of the communication cost.

Two-Stage Focused Inference for Resource-Constrained Minimal Collision Navigation

The operation of mobile robots in unknown environments typically requires building maps during exploration. As the exploration time and environment size increase, the amount of data collected and the number of variables required to represent these maps both grow, which is problematic since all real robots have finite resources. The solution proposed in this paper is to only retain the variables and measurements that are most important to achieve the robots task. The variable and measurement selection approach is demonstrated on the task of navigation with a low risk of collision. Our approach has two stages: first, a subset of the variables is selected that is most useful for minimizing the uncertainty of navigation (termed the “focused variables”). And second, a task-agnostic method is used to select a subset of the measurements that maximizes the information over these focused variables (“focused inference”). Detailed simulations and hardware experiments show that the two-stage approach constrains the number of variables and measurements. It can generate much sparser maps than existing approaches in the literature, while still achieving a better task performance—in this case (fewer collisions). An incremental and iterative approach is further presented, in which the two-stage procedure is performed on subsets of the data, and thus, avoids the necessity of performing a resource-intensive batch selection on large datasets.

Importance-Weighted Adaptive Search for Multi-Class Targets

In sparse target inference problems, it has been shown that significant gains can be achieved by adaptive sensing using convex criteria. We generalize this previous work on adaptive sensing to: a) include multiple classes of targets with different levels of importance and b) accommodate multiple sensor models. Optimization policies are developed to allocate a limited resource budget to simultaneously locate, classify and estimate a sparse number of targets embedded in a large space. Bounds on the performance of the proposed policies are derived by analyzing a baseline policy, which allocates resources uniformly across the scene, and an oracle policy which has a priori knowledge of the target locations/classes. These bounds quantify the potential benefit of adaptive sensing as a function of target frequency and importance. Numerical results indicate that the proposed policies perform close to the oracle bound when signal quality is sufficiently high. Moreover, the proposed policies improve on previous policies in terms of reducing estimation error, reducing misclassification probability, and increasing expected return. To account for sensors with different levels of agility, three sensor models are considered: global adaptive (GA), which can allocate different amounts of resource to each location in the space; global uniform (GU), which can allocate resources uniformly across the scene; and local adaptive (LA), which can allocate fixed units to a subset of locations. Policies that use a mixture of GU and LA sensors are shown to perform similarly to those that use GA sensors while being more easily implementable.