M. Ani Hsieh

Maintaining network connectivity and performance in robot teams

In this paper, we present an experimental study of strategies for maintaining end-to-end communication links for tasks such as surveillance, reconnaissance, and target search and identification, where team connectivity is required for situational awareness. Our main contributions are threefold: (a) We present the construction of a radio signal strength map that can be used to plan multi-robot tasks, and also serve as useful perceptual information. We show how a nominal model of an urban environment obtained by aerial surveillance, is used to generate strategies for exploration. (b) We present reactive controllers for communication link maintenance; and (c) we consider the differences between monitoring signal strength versus data throughput. Experimental results, obtained using our multi-robot testbed in three representative urban environments are presented with each of our main contributions.

Adaptive Teams of Autonomous Aerial and Ground Robots for Situational Awareness

This is a preprint of an article accepted for publication in the Journal of Field Robotics, copyright 2007. Journal of Field Robotics 24(11), 991–1014 (2007)

Decentralized controllers for shape generation with robotic swarms

We address the synthesis of controllers for a swarm of robots to generate a desired two-dimensional geometric pattern specified by a simple closed planar curve with local interactions for avoiding collisions or maintaining specified relative distance constraints. The controllers are decentralized in the sense that the robots do not need to exchange or know each others state information. Instead, we assume that the robots have sensors allowing them to obtain information about relative positions of neighbors within a known range. We establish stability and convergence properties of the controllers for a certain class of simple closed curves. We illustrate our approach through simulations and consider extensions to more general planar curves.

An Optimal Approach to Collaborative Target Tracking with Performance Guarantees

In this paper, we present a discrete-time optimization framework for target tracking with multi-agent systems. The “target tracking” problem is formulated as a generic semidefinite program (SDP) that when paired with an appropriate objective yields an optimal robot configuration over a given time step. The framework affords impressive performance guarantees to include full target coverage (i.e. each target is tracked by at least a single team member) as well as maintenance of network connectivity across the formation. Key to this work is the result from spectral graph theory that states the second-smallest eigenvalue—λ2—of a weighted graph’s Laplacian (i.e. its inter-connectivity matrix) is a measure of connectivity for the associated graph. Our approach allows us to articulate agent-target coverage and inter-agent communication constraints as linear-matrix inequalities (LMIs). Additionally, we present two key extensions to the framework by considering alternate tracking problem formulations. The first allows us to guarantee k-coverage of targets, where each target is tracked by k or more agents. In the second, we consider a relaxed formulation for the case when network connectivity constraints are superfluous. The problem is modeled as a second-order cone program (SOCP) that can be solved significantly more efficiently than its SDP counterpart—making it suitable for large-scale teams (e.g. 100’s of nodes in real-time). Methods for enforcing inter-agent proximity constraints for collision avoidance are also presented as well as simulation results for multi-agent systems tracking mobile targets in both ℝ2 and ℝ3.

Robotic Tracking of Coherent Structures in Flows

Lagrangian coherent structures (LCSs) are separatrices that delineate dynamically distinct regions in general dynamical systems and can be viewed as the extensions of stable and unstable manifolds to general time-dependent systems. Identifying LCS in dynamical systems is useful for many applications, including oceanography and weather prediction. In this paper, we present a collaborative robotic control strategy that is designed to track stable and unstable manifolds in dynamical systems, including ocean flows. The technique does not require global information about the dynamics, and is based on local sensing, prediction, and correction. The collaborative control strategy is implemented with a team of three robots to track coherent structures and manifolds on static flows, a time-dependent model of a wind-driven double-gyre flow often seen in the ocean, experimental data that are generated by a flow tank, and actual ocean data. We present simulation results and discuss theoretical guarantees of the collaborative tracking strategy.

Distributed Robot Ensemble Control for Deployment to Multiple Sites

We address the ensemble synthesis of distributed control policies to allocate a team of homogenous robots to a collection of spatially distributed tasks. We assume individual robot controllers are derived via the sequential composition of individual task controllers and develop an appropriate macroscopic description of the team dynamics. A feedback control strategy is synthesized using the macroscopic model to enable the team to maintain a desired distribution of robots across the various tasks by controlling the mean and the variance of the robot population at each task. We present a distributed implementation of the proposed ensemble feedback strategy with minimal communication requirements. We establish stability properties of our ensemble controller and verify the feasibility of the distributed ensemble controller through high-fidelity simulations.

Robotic manifold tracking of coherent structures in flows

Tracking Lagrangian coherent structures in dynamical systems is important for many applications such as oceanography and weather prediction. In this paper, we present a collaborative robotic control strategy designed to track stable and unstable manifolds. The technique does not require global information about the fluid dynamics, and is based on local sensing, prediction, and correction. The collaborative control strategy is implemented on a team of three robots to track coherent structures and manifolds on static flows as well as a noisy time-dependent model of a wind-driven double-gyre often seen in the ocean. We present simulation and experimental results and discuss theoretical guarantees of the collaborative tracking strategy.

Macroscopic modeling of stochastic deployment policies with time delays for robot ensembles

We consider the dynamic assignment and reassignment of a homogeneous robot ensemble to multiple spatially located tasks with deterministic or near-deterministic task execution times. Similar to Halasz et al. and Berman et al., we consider the development of agent-level, i.e. microscopic, stochastic control policies through the analysis of an appropriate macroscopic analytical model that describes the dynamics of the ensemble. Specifically, we present an approach to better approximate the effects of deterministic microscopic time delays at the macroscopic level based on Padé approximants. We present, analyze, and compare the frequency response of our approach to that presented by Berman et al. using different agent-based simulations.

Constrained Task Partitioning For Distributed Assembly

We address the distributed assembly of a structure by a team of homogeneous robots. We present an algorithm to partition 2- and 3-D assembly tasks into N separate subtasks that satisfy local and global precedence constraints between the assembly components. The objective is to achieve a partitioning that minimizes the workload imbalance between the robots and maximizes assembly parallelization. The algorithm consists of three phases: 1) an initial allocation of the subtasks to each robot via a variant of Dijkstras algorithm; 2) a component trading protocol to balance each robots workload without violating any constraints; and 3) the creation of an assembly plan for each robot that minimizes conflicts during execution. We present simulation results for three variants of the algorithm and experiments using our multi-robot testbed.

Specialization as an optimal strategy under varying external conditions

We present an investigation of specialization when considering the execution of collaborative tasks by a robot swarm. Specifically, we consider the stick-pulling problem first proposed by Martinoli et al. [1], [2] and develop a macroscopic analytical model for the swarm executing a set of tasks that require the collaboration of two robots. We show, for constant external conditions, maximum productivity can be achieved by a single species swarm with carefully chosen operational parameters. While the same applies for a two species swarm, we show how specialization is a strategy best employed for changing external conditions.