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.
Our problem in this paper is also related to sweep coverage, or lawn mowing and milling problems, in which robots with finite sensor footprints move over an environment so that every point in the environment is visited at least once by a robot. Lawn mowing and milling has been treated in @cite_27 and other works. Sweep coverage has recently been studied in @cite_9 , and in @cite_12 efficient sweep coverage algorithms are proposed for ant robots. A survey of sweep coverage is given in @cite_3 . Our problem is significantly different from these because our environment is dynamic, thereby requiring continual re-milling or re-sweeping. A different notion of persistent surveillance has been considered in @cite_35 and @cite_28 , where a persistent task is defined as one whose completion takes much longer than the life of a robot. While the terminology is similar, our problem is more concerned with the task (sweeping or monitoring) itself than with power requirements of individual robots.
@cite_35 @cite_28 @cite_9 @cite_3 @cite_27 @cite_12
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As mobile robots become increasingly autonomous over extended periods of time, opportunities arise for their use on repetitive tasks. We define and implement behaviors for a class of such tasks that we call continuous area sweeping tasks. A continuous area sweeping task is one in which a group of robots must repeatedly visit all points in a fixed area, possibly with nonuniform frequency, as specified by a task-dependent cost function. Examples of problems that need continuous area sweeping are trash removal in a large building and routine surveillance. In our previous work we have introduced a single-robot approach to this problem. In this paper, we extend that approach to multi-robot scenarios. The focus of this paper is adaptive and decentralized task assignment in continuous area sweeping problems, with the aim of ensuring stability in environments with dynamic factors, such as robot malfunctions or the addition of new robots to the team. Our proposed negotiation-based approach is fully implemented and tested both in simulation and on physical robots In this paper, we address the problem of persistently covering an environment with a group of mobile robots. In contrast to traditional coverage, in our scenario the coverage level of the environment is always changing. For this reason, the robots have to continually move to maintain a desired coverage level. In this context, our contribution is a complete approach to the problem, including distributed estimation of the coverage and control of the motion of the robots. First, we present an algorithm that allows every robot to estimate the global coverage function only with local information. We pay special attention to the characterization of the algorithm, establishing bounds on the estimation error, and we demonstrate that the algorithm guarantees a perfect estimation in particular areas. Second, we introduce a new function to determine the possible improvement of the coverage at each point of the environment. Upon this metric, we build a motion control strategy that drives the robots to the points of the highest improvement while following the direction of the gradient of the function. Finally, we simulate the proposal to test its correctness and performance. This paper surveys recent results in coverage path planning, a new path planning approach that determines a path for a robot to pass over all points in its free space. Unlike conventional point-to-point path planning, coverage path planning enables applications such as robotic de-mining, snow removal, lawn mowing, car-body painting, machine milling, etc. This paper will focus on coverage path planning algorithms for mobile robots constrained to operate in the plane. These algorithms can be classified as either heuristic or complete. It is our conjecture that most complete algorithms use an exact cellular decomposition, either explicitly or implicitly, to achieve coverage. Therefore, this paper organizes the coverage algorithms into four categories: heuristic, approximate, partial-approximate and exact cellular decompositions. The final section describes some provably complete multi-robot coverage algorithms. In this letter, we consider the problem of rendering robotic tasks persistent by ensuring that the robots' energy levels are never depleted, which means that the tasks can be executed over long time horizons. This process is referred to as the persistification of the task. In particular, the state of each robot is augmented with its battery level so that the desired persistent behavior can be encoded as the forward invariance of a set such that the robots never deplete their batteries. Control barrier functions are employed to synthesize controllers that ensure that this set is forward invariant and, therefore, that the robotic task is persistent. As an application, this letter considers the persistification of a robotic sensor coverage task in which a group of robots has to cover an area of interest. The successful persistification of the coverage task is shown in simulation and on a team of mobile robots.