David Saldana

Resilient Flocking for Mobile Robot Teams

We present a method that enables resilient formation control for mobile robot teams in the presence of noncooperative (defective or malicious) robots. Recent results in network science define graph topological properties that guarantee resilience against faults and attacks on individual nodes in static networks. We build on these results to propose a control policy that allows a team of mobile robots to achieve resilient consensus on the direction of motion. Our strategy relies on dynamic connectivity management that makes use of a metric that characterizes the robustness of the communication network topology. Our method distinguishes itself from prior work in that our connectivity management strategy ensures that the network lies above a critical resilience threshold, guaranteeing that the consensus algorithm always converges to a value within the range of the cooperative agents’ initial values. We demonstrate the use of our framework for resilient flocking, and show simulation results with groups of holonomic mobile robots.

Resilient consensus for time-varying networks of dynamic agents

We consider networks of dynamic agents that execute cooperative, distributed control algorithms in order to coordinate themselves and to collectively achieve goals. The agents rely on consensus algorithms that are based on local interactions with their nearest neighbors in the communication graph. However, such systems are not robust to one or more malicious agents and there are no performance guarantees when one or more agents do not cooperate. Recent results in network science deal with this problem by requiring specific graph topological properties. Nevertheless, the required network topologies imply high connectivity levels, which may be difficult to achieve in systems that exhibit time-varying communication graphs. In this paper, we propose an approach that provides resilience for networks of dynamic agents whose communication graphs are time-varying. We show that in the case where the required connectivity constraints cannot be satisfied at all times, we can resort to a consensus protocol that guarantees resilience when the union of communication graphs over a bounded period of time satisfies certain robustness properties. We propose a control policy to attain resilient behavior in the context of perimeter surveillance with a team of robots. We provide simulations that support our theoretical analyses.

Dynamic perimeter surveillance with a team of robots

In this paper, we propose a motion planning method to escort a set of agents from one place to a goal in an environment with obstacles. The agents are distributed in a finite area, with a time-varying perimeter, in which we put multiple robots to patrol around it with a desired velocity. Our proposal is composed of two parts. The first one generates a plan to move and deform the perimeter smoothly, and as a result, we obtain a twice differentiable boundary function. The second part uses the boundary function to compute a trajectory for each robot, we obtain each resultant trajectory by first solving a differential equation. After receiving the boundary function, the robots do not need to communicate among themselves until they finish their trajectories. We validate our proposal with simulations and experiments with actual robots.

A distributed multi-robot approach for the detection and tracking of multiple dynamic anomalies

In many cases, large area disasters could be possibly be prevented if the incipient small-scale anomalies are detected in their early stages. A way to accomplish this would be to have multiple sensors deployed in disaster prone areas to detect anomalies. However, compared to static sensor networks, robotic sensor networks offer advantages such as active sensing, large area coverage and anomaly tracking. This paper addresses the problem of coordinating and controlling multiple robots for the detection of multiple dynamic anomalies in the environment. The main contribution of the work is a combined approach for the effective exploration under uncertainty, the anomaly tracking, and the autonomous on-line allocation of agents. Robots explore the work area maintaining the history of the sensed areas to reduce redundancy and to allow for full-map coverage. When an anomaly is detected, a robot autonomously determines how to either track the anomaly or to continue the exploration of the environment, depending on the size of the anomaly, which is estimated by the length of the perimeter of the enclosing polygon. We show results of our methodology both in simulation and with actual robots which have demonstrated that robots can autonomously and distributively be allocated to track or to explore depending on the behavior of the detected anomalies.

Triangular Networks for Resilient Formations.

Consensus algorithms allow multiple robots to achieve agreement on estimates of variables in a distributed manner, hereby coordinating the robots as a team, and enabling applications such as formation control and cooperative area coverage. These algorithms achieve agreement by relying only on local, nearest-neighbor communication. The problem with distributed consensus, however, is that a single malicious or faulty robot can control and manipulate the whole network. The objective of this paper is to propose a formation topology that is resilient to one malicious node, and that satisfies two important properties for distributed systems: (i) it can be constructed incrementally by adding one node at a time in such a way that the conditions for attachment can be computed locally, and (ii) its robustness can be verified through a distributed method by using only neighborhood-based information. Our topology is characterized by triangular robust graphs, consists of a modular structure, is fully scalable, and is well suited for applications of large-scale networks. We describe how our proposed topology can be used to deploy networks of robots. Results show how triangular robust networks guarantee asymptotic consensus in the face of a malicious agent.

Distributed multi-robot coordination for dynamic perimeter surveillance in uncertain environments

In this work, multiple robots circulate around the boundary of a desired region in order to create a virtual fence. The aim of the this fence is to avoid internal or external agents crossing through the delimited area. In this paper, we propose a distributed technique that allows a team of robots to plan the deformation of the boundary shape in order to escort the safe region from one place to a goal. Our proposal is composed of two parts. First, we present a distributed planning method for the dynamic boundary. We model the resulting plan as a twice differentiable function. Second, we use the obtained function to guide the robot team, where every member uses only local information for the controller. The robots distribute themselves along the time-varying perimeter and patrol around it. We show in simulation how the robots behave in partially/totally unknown environments with static obstacles.

Predicting Environmental Boundary Behaviors With a Mobile Robot

Predicting the behavior of dangerous environmental boundaries, like spreading fire or oil spill, provides relevant information to mitigate the problem or even to support evacuation actions in order to save human or animal lives. In this letter, we present a model that uses a single robot moving around an environmental boundary in order to predict its shape by an analytical continuous function, which is based on the combination of polynomial approximation and Fourier Series. We show that the method converges to the exact boundary when we increase the sample frequency and the robot velocity. In order to evaluate the estimation quality, we performed experiments with simulated and actual robots. We applied our model in some dynamic boundaries presented in the literature, as in the application of plume-front estimation, showing that it accomplish accurate results.

Detecting Latent Variables of Interest in Geo-Localized Environments Using an Aerial Robot

In general, monitoring applications require human intervention whenever there is no physical sensors for the variables of interest (e.g. People in danger after a catastrophe). In this paper we describe an inference engine which is used to estimate latent variables that can not be perceived by sampling the physical phenomena directly. Our approach uses information from different types of sensors, and fuses them along with knowledge of experts. The inference engine works with probabilistic first order logic rules based on geo-located sensed data as evidences in order to dynamically create the structure of a Bayesian network. Our experiments, performed by using an aerial robot with a mounted RGB-Camera, show the capability of our method to detect people in danger situations, where the physical variables to being sensed are humans and fire.

Searching for Regions Out of Normal Conditions Using a Team of Robots

Searching for regions in abnormal conditions is a priority in environments susceptible to catastrophes (e.g. forest fires or oil spills). Those disasters usually begin with an small anomaly that may became unsustainable if it is not detected at an early stage. We propose a probabilistic technique to coordinate multiple robots in perimeter searching and tracking, which are fundamental tasks if they are to detect and follow anomalies in an environment. The proposed method is based on a particle filter technique, which uses multiple robots to fuse distributed sensor information and estimate the shape of an anomaly. Complementary sensor fusion is used to coordinate robot navigation and reduce detection time when an anomaly arises. Validation of our approach is obtained both in simulation and with real robots. Five different scenarios were designed to evaluate and compare the efficiency in both exploration and tracking tasks. The results have demonstrated that when compared to state-of-the art methods in the literature, the proposed method is able to search anomalies under uncertainty and reduce the detection time by automatically increasing the number of robots.