Search for: All records

The Weighted-Mean Subsequence Reduced (W-MSR) algorithm, the state-of-the-art method for Byzantine-resilient design of decentralized multi-robot systems, is based on discarding outliers received over Linear Consensus Protocol (LCP). Although W-MSR provides theoretical guarantees relating network connectivity to the convergence of the underlying consensus, W-MSR comes with several limitations: the number of Byzantine robots, 𝐹 , to tolerate should be known a priori, each robot needs to maintain 2𝐹 + 1 neighbors, 𝐹 + 1 robots must independently make local measurements of the consensus property in order for the swarm’s decision to change, and W-MSR is specific to LCP and does not generalize to applications not implemented over LCP. In this work, we pro- pose a Decentralized Blocklist Protocol (DBP) based on inter-robot accusations. Accusations are made on the basis of locally-made observations of misbehavior, and once shared by cooperative robots across the network are used as input to a graph matching algorithm that computes a blocklist. DBP generalizes to applications not implemented via LCP, is adaptive to the number of Byzantine robots, and allows for fast information propagation through the multi- robot system while simultaneously reducing the required network connectivity relative to W-MSR. On LCP-type applications, DBP reduces the worst-case connectivity requirement of W-MSR from (2𝐹 + 1)-connected to (𝐹 + 1)-connected and the minimum number of cooperative observers required to propagate new information from 𝐹 + 1 to just 1 observer. We demonstrate that our approach to Byzantine resilience scales to hundreds of robots on target tracking, time synchronization, and localization case studies. 

In centralized multi-robot systems, a central entity (CE) checks that robots follow their assigned motion plans by comparing their expected location to the location they self-report. We show that this self-reporting monitoring mechanism is vulnerable to plan- deviation attacks where compromised robots don’t follow their assigned plans while trying to conceal their movement by misreporting their location. We propose a two-pronged mitigation for plan-deviation attacks: (1) an attack detection technique leveraging both the robots’ local sensing capabilities to report observations of other robots and co-observation schedules generated by the CE, and (2) a prevention technique where the CE issues horizon-limiting announcements to the robots, reducing their instantaneous knowledge of forward lookahead steps in the global motion plan. On a large-scale automated warehouse benchmark, we show that our solution enables attack prevention guarantees from a stealthy attacker that has compromised multiple robots. 

In this paper, we focus on using path planning and inter-agent measurements to improve the security of multi-robot systems against possible takeovers from cyber-attackers. We build upon recent trajectory optimization approaches where introspective measurement capabilities of the robots are used in an co-observation schedule to detect deviations from the preordained routes. This paper proposes additional constraints that can be incorporated in the previous trajectory optimization algorithm based on Alternating Direction Method of Multipliers (ADMM). The new constraints provide guarantees that a compromised robot cannot reach a designed safety zone between observations despite adversarial movement by the attacker. We provide a simulation showcasing the new components of the formulation in a multi-agent map exploration task with several safety zones. 

This paper investigates the aeroacoustic interactions of small hovering rotors, using both experiments and computations. The experiments were conducted in an anechoic chamber with arrays of microphones setup to evaluate the azimuthal and polar directivity. The computational methodology consists of high fidelity detached eddy simulations coupled to the Ffowcs-Williams and Hawkings equation, supplemented by a trailing edge broadband noise code. The aerodynamics and aeroacoustics of a single rotor are investigated first. The simulations capture a Reynolds number effect seen in the performance parameters that results in the coefficient of thrust changing with the RPM. The acoustic analysis enables the identification of self-induced noise sources. Next, dual side-by-side rotors are studied in both counter-rotating and co-rotating configurations to quantify the impact of their interactions. Higher harmonics appear due to the interactions and it is verified that the counter-rotating case leads to more noise and a less uniform azimuthal directivity. Difficulties that arise when trying to validate small rotor calculations against experiments are discussed. Comparisons of computational and experimental results yield further insight into the noise mechanisms that are captured by each methodology. 

We present a novel haptic teleoperation approach that considers not only the safety but also the stability of a teleoperation system. Specifically, we build upon previous work on haptic shared control, which generates a reference haptic feedback that helps the human operator to safely navigate the robot but without taking away their control authority. Crucially, in this approach the force rendered to the user is not directly reflected in the motion of the robot (which is still directly controlled by the user); however, previous work in the area neglected to consider the possible instabilities in feedback loop generated by a user that over-responds to the haptic force. In this paper we introduce a differential constraint on the rendered force that makes the system finite-gain L2 stable; the constraint results in a Quadratically Constrained Quadratic Program (QCQP), for which we provide a closed-form solution. Our constraint is related to, but less restrictive than, the typical passivity constraint used in previous literature. We conducted an experimental simulation in which a human operator flies a UAV near an obstacle to evaluate the proposed method. 

Applications of micro unmanned aerial vehicles (UAVs) are gradually expanding into complex urban and natural environments. Despite noticeable progress, flying robots in obstacle-rich environments is still challenging. On-board processing for detecting and avoiding obstacles is possible, but at a significant computational expense, and with significant limitations (e.g., for obstacles with small cross sections, such as wires). A low-cost alternative is to mitigate physical contacts through a cage or other similar protective devices. In this paper, we propose to transform these passive protective devices into functional sensors: we introduce a suspended rim combined with a central base measuring the relative displacement of the rim; we provide a full mechanical design, and derive solutions to the inverse kinematics for recovering the collision direction in real time. As a proof of concept, we show the benefits of this novel form of sensing by embedding it in a traditional particle filter for self-localization in a known environment; our experiments show that localization is possible with a minimal sacrifice in payload capacity.