Mac Schwager

Decentralized, Adaptive Coverage Control for Networked Robots

A decentralized, adaptive control law is presented to drive a network of mobile robots to an optimal sensing configuration. The control law is adaptive in that it uses sensor measurements to learn the distribution of sensory information in the environment. It is decentralized in that it requires only information local to each robot. The controller is then improved by introducing a consensus algorithm to propagate sensory information from every robot throughout the network. Convergence and consensus of parameters is proven with a Lyapunovtype proof. The controller with and without consensus is demonstrated in numerical simulations. These techniques are suggestive of broader applications of adaptive control methodologies to decentralized control problems in unknown dynamic environments.

Persistent Robotic Tasks: Monitoring and Sweeping in Changing Environments

In this paper, we present controllers that enable mobile robots to persistently monitor or sweep a changing environment. The environment is modeled as a field that is defined over a finite set of locations. The field grows linearly at locations that are not within the range of a robot and decreases linearly at locations that are within range of a robot. We assume that the robots travel on given closed paths. The speed of each robot along its path is controlled to prevent the field from growing unbounded at any location. We consider the space of speed controllers that are parametrized by a finite set of basis functions. For a single robot, we develop a linear program that computes a speed controller in this space to keep the field bounded, if such a controller exists. Another linear program is derived to compute the speed controller that minimizes the maximum field value over the environment. We extend our linear program formulation to develop a multirobot controller that keeps the field bounded. We characterize, both theoretically and in simulation, the robustness of the controllers to modeling errors and to stochasticity in the environment.

Voronoi coverage of non-convex environments with a group of networked robots

This paper presents a solution to decentralized Voronoi coverage in non-convex polygonal environments. We show that complications arise when existing approaches to Voronoi coverage are applied for deploying a group of robots in non-convex environments. We present an algorithm that is guaranteed to converge to a local optimum. Our algorithm combines classical Voronoi coverage with the Lloyd algorithm and the local path planning algorithm TangentBug to compute the motion of the robots around obstacles and corners. We present the algorithm and prove convergence and optimality. We also discuss experimental results from an implementation with five robots.

Distributed Coverage Control with Sensory Feedback for Networked Robots.

This paper presents a control strategy that allows a group of mobile robots to position themselves to optimize the measurement of sensory information in the environment. The robots use sensed information to estimate a function indicating the relative importance of different areas in the environment. Their estimate is then used to drive the network to a desirable placement configuration using a computationally simple decentralized control law. We formulate the problem, provide a practical control solution, and present the results of numerical simulations. We then discuss experiments carried out on a swarm of mobile robots.

Unifying geometric, probabilistic, and potential field approaches to multi-robot deployment

This paper unifies and extends several different existing strategies for deploying groups of robots in an environment. A cost function is proposed that can be specialized to represent widely different multi-robot deployment tasks. It is shown that geometric and probabilistic deployment strategies that were previously seen as distinct are in fact related through this cost function, and differ only in the value of a single parameter. These strategies are also related to potential field-based controllers through the same cost function, though the relationship is not as simple. Distributed controllers are then obtained from the gradient of the cost function and are proved to converge to a local minimum of the cost function. Three special cases are derived as examples: a Voronoi-based coverage control task, a probabilistic minimum variance task, and a task using artificial potential fields. The performance of the three different controllers are compared in simulation. A result is also proved linking multi-robot deployment to non-convex optimization problems, and multi-robot consensus (i.e. all robots moving to the same point) to convex optimization problems, which implies that multi-robot deployment is inherently more difficult than multi-robot consensus.

Distributed robotic sensor networks: An information-theoretic approach

In this paper we present an information-theoretic approach to distributively control multiple robots equipped with sensors to infer the state of an environment. The robots iteratively estimate the environment state using a sequential Bayesian filter, while continuously moving along the gradient of mutual information to maximize the informativeness of the observations provided by their sensors. The gradient-based controller is proven to be convergent between observations and, in its most general form, locally optimal. However, the computational complexity of the general form is shown to be intractable, and thus non-parametric methods are incorporated to allow the controller to scale with respect to the number of robots. For decentralized operation, both the sequential Bayesian filter and the gradient-based controller use a novel consensus-based algorithm to approximate the robots’ joint measurement probabilities, even when the network diameter, the maximum in/out degree, and the number of robots are unknown. The approach is validated in two separate hardware experiments each using five quadrotor flying robots, and scalability is emphasized in simulations using 100 robots.

Decentralized, Adaptive Control for Coverage with Networked Robots

A decentralized, adaptive control law is presented to drive a network of mobile robots to a near-optimal sensing configuration. The control law is adaptive in that it integrates sensor measurements to provide a converging estimate of the distribution of sensory information in the environment. It is decentralized in that it requires only information local to each robot. A Lyapunov-type proof is used to show that the control law causes the network to converge to a near-optimal sensing configuration, and the controller is demonstrated in numerical simulations. This technique suggests a broader application of adaptive control methodologies to decentralized control problems in unknown dynamical environments.

Simultaneous Coverage and Tracking (SCAT) of Moving Targets with Robot Networks

We address the problem of simultaneously covering an environment and tracking intruders (SCAT). The problem is translated to the task of covering environments with time-varying density functions under the locational optimization framework. This allows for coupling the basic subtasks: task assignment, coverage, and tracking. A decentralized controller with guaranteed exponential convergence is devised. The SCAT algorithm is verified in simulations and on a team of robots.

Optimal coverage for multiple hovering robots with downward facing cameras

This paper presents a distributed control strategy for deploying hovering robots with multiple downward facing cameras to collectively monitor an environment. Information per pixel is proposed as an optimization criterion for multi-camera placement problems. This metric is used to derive a specific cost function for multiple downward facing cameras mounted on hovering robot platforms. The cost function leads to a gradient-based distributed controller for positioning the robots. A convergence proof using LaSalles invariance principle is given to show that the robots converge to locally optimal positions. The controller is demonstrated in experiments with three flying quad-rotor robots.

A ladybug exploration strategy for distributed adaptive coverage control

A control strategy inspired by the hunting tactics of ladybugs is presented to simultaneously achieve sensor coverage and exploration of an area with a group of networked robots. The controller is distributed in that it requires only information local to each robot, and adaptive in that it modifies its behavior based on information in the environment. The ladybug controller is developed as a modification to a basic coverage control law, first for the non-adaptive case, then for the adaptive case. Stability is proven for both cases with a Lyapunov-type proof. Results of numerical simulations are presented.