Ninad Jadhav, Weiying Wang, Diana Zhang, Oussama Khatib, Swarun Kumar, and Stephanie Gil. 9/26/2022. “
A wireless signal-based sensing framework for robotics.” International Journal of Robotics Research, 2022, Volume 41, Issue 11-12, Pp. 955–992.
Publisher's VersionAbstractIn this paper, we develop the analytical framework for a novel Wireless signal-based Sensing capability for Robotics (WSR) by leveraging robots' mobility in 3D space. It allows robots to primarily measure relative direction, or Angle-of-Arrival (AOA), to other robots, while operating in non-line-of-sight unmapped environments and without requiring external infrastructure. We do so by capturing all of the paths that a wireless signal traverses as it travels from a transmitting to a receiving robot in the team, which we term as an AOA profile. The key intuition behind our approach is to enable a robot to emulate antenna arrays as it moves freely in 2D and 3D space. The small differences in the phase of the wireless signals are thus processed with knowledge of robots' local displacement to obtain the profile, via a method akin to Synthetic Aperture Radar (SAR). The main contribution of this work is the development of i) a framework to accommodate arbitrary 2D and 3D motion, as well as continuous mobility of both signal transmitting and receiving robots, while computing AOA profiles between them and ii) a Cramer-Rao Bound analysis, based on antenna array theory, that provides a lower bound on the variance in AOA estimation as a function of the geometry of robot motion. This is a critical distinction with previous work on SAR-based methods that restrict robot mobility to prescribed motion patterns, do not generalize to the full 3D space, and require transmitting robots to be stationary during data acquisition periods. We show that allowing robots to use their full mobility in 3D space while performing SAR results in more accurate AOA profiles and thus better AOA estimation. We formally characterize this observation as the informativeness of the robots' motion; a computable quantity for which we derive a closed form. All analytical developments are substantiated by extensive simulation and hardware experiments on air/ground robot platforms using 5GHz WiFi. Our experimental results bolster our analytical findings, demonstrating that 3D motion provides enhanced and consistent accuracy, with a total AOA error of less than 10 degree for 95% of trials. We also analytically characterize the impact of displacement estimation errors on the measured AOA, and validate this theory empirically using robot displacements obtained using an off-the-shelf Intel Tracking Camera T265. Finally, we demonstrate the performance of our system on a multi-robot task where a heterogeneous air/ground pair of robots continuously measure AOA profiles over a WiFi link to achieve dynamic rendezvous in an unmapped, 300m2 environment with occlusions.
author_version.pdf Michal Yemini, Stephanie Gil, and Andrea J. Goldsmith. 8/23/2022. “
Cloud-Cluster Architecture for Detection in Intermittently Connected Sensor Networks.” IEEE Transactions on Wireless Communications, 1536-1276, Pp. 1.
Publisher's VersionAbstractWe consider a centralized detection problem where sensors experience noisy measurements and intermittent connectivity to a centralized fusion center. The sensors collaborate locally within predefined sensor clusters and fuse their noisy sensor data to reach a common local estimate of the detected event in each cluster. The connectivity of each sensor cluster is intermittent and depends on the available communication opportunities of the sensors to the fusion center. Upon receiving the estimates from all the connected sensor clusters the fusion center fuses the received estimates to make a final determination regarding the occurrence of the event across the deployment area. We refer to this hybrid communication scheme as a cloud-cluster architecture. We propose a method for optimizing the decision rule for each cluster and analyzing the expected detection performance resulting from our hybrid scheme. Our method is tractable and addresses the high computational complexity caused by heterogeneous sensors’ and clusters’ detection quality, heterogeneity in their communication opportunities, and non-convexity of the loss function. Our analysis shows that clustering the sensors provides resilience to noise in the case of low sensor communication probability with the cloud. For larger clusters, a steep improvement in detection performance is possible even for a low communication probability by using our cloud-cluster architecture.
cloud-cluster_architecture_for_detection_in_intermittently_connected_sensor_networks.pdf Matthew Cavorsi, Ninad Jadhav, David Saldaña, and Stephanie Gil. 2022. “
Adaptive malicious robot detection in dynamic topologies.” In IEEE Conference on Decisions and Control (CDC). Cancun, Mexico.
AbstractWe consider a class of problems where robots
gather observations of each other to assess the legitimacy
of their peers. Previous works propose accurate detection of
malicious robots when robots are able to extract observations of
each other for a long enough time. However, they often consider
static networks where the set of neighbors a robot observes
remains the same. Mobile robots experience a dynamic set of
neighbors as they move, making the acquisition of adequate
observations more difficult. We design a stochastic policy that
enables the robots to periodically gather observations of every
other robot, while simultaneously satisfying a desired robot
distribution over an environment modeled by sites. We show
that with this policy, any pre-existing or new malicious robot in
the network will be detected in a finite amount of time, which
we minimize and also characterize. We derive bounds on the
time needed to obtain the desired number of observations for a
given topological map and validate these bounds in simulation.
We also show and verify in a hardware experiment that the
team is able to successfully detect malicious robots, and thus
estimate the true distribution of cooperative robots per site, in
order to converge to the desired robot distribution over sites.
Matthew Cavorsi, Beatrice Capelli, Lorenzo Sabattini, and Stephanie Gil. 2022. “
Multi-robot adversarial resilience using control barrier functions.” In Robotics Science and Systems (RSS) Conference.
Abstract
In this paper we present a control barrier function-based (CBF) resilience controller that provides resilience in a multi-robot network to adversaries. Previous approaches provide resilience by virtue of specific linear combinations of multiple control constraints. These combinations can be difficult to find and are sensitive to the addition of new constraints. Unlike previous approaches, the proposed CBF provides network resilience and is easily amenable to multiple other control constraints, such as collision and obstacle avoidance. The inclusion of such constraints is essential in order to implement a resilience controller on realistic robot platforms. We demonstrate the viability of the CBF-based resilience controller on real robotic systems through case studies on a multi-robot flocking problem in cluttered environments with the presence of adversarial robots.
Matthew Cavorsi and Stephanie Gil. 2022. “
Providing local resilience to vulnerable areas in robotic networks.” In IEEE International Conference on Robotics and Automation (ICRA), Pp. 4929-4935. Philadelphia, PA.
AbstractWe study how information flows through a multirobot network in order to better understand how to provide resilience to malicious information. While the notion of global resilience is well studied, one way existing methods provide global resilience is by bringing robots closer together to improve the connectivity of the network. However, large changes in network structure can impede the team from performing other functions such as coverage, where the robots need to spread apart. Our goal is to mitigate the trade-off between resilience and network structure preservation by applying resilience locally in areas of the network where it is needed most. We introduce a metric, Influence, to identify vulnerable regions in the network requiring resilience. We design a control law targeting local resilience to the vulnerable areas by improving the connectivity of robots within these areas so that each robot has at least 2F +1 vertex-disjoint communication paths between itself and the high influence robot in the vulnerable area. We demonstrate the performance of our local resilience controller in simulation and in hardware by applying it to a coverage problem and comparing our results with an existing global resilience strategy. For the specific hardware experiments, we show that our control provides local resilience to vulnerable areas in the network while only requiring 9.90% and 15.14% deviations from the desired team formation compared to the global strategy.
Michal Yemini, Angelia Nedi´c, Andrea J. Goldsmith, and Stephanie Gil. 2022. “
Resilience to Malicious Activity in Distributed Optimization for Cyberphysical Systems .” In IEEE Conference on Decision and Control.
Publisher's VersionAbstractEnhancing resilience in distributed networks in the face of malicious agents is an important problem for which many key theoretical results and applications require further development and characterization. This work focuses on the problem of distributed optimization in multi-agent cyberphysical systems, where a legitimate agent’s dynamic is influenced both by the values it receives from potentially malicious neighboring agents, and by its own self-serving target function. We develop a new algorithmic and analytical framework to achieve resilience for the class of problems where stochastic values of trust between agents exist and can be exploited. In this case we show that convergence to the true global optimal point can be recovered, both in mean and almost surely, even in the presence of malicious agents. Furthermore, we provide expected convergence rate guarantees in the form of upper bounds on the expected squared distance to the optimal value. Finally, we present numerical results that validate the analytical convergence guarantees we present in this paper even when the malicious agents compose the majority of agents in the network.
Ninad Jadhav, Weiying Wang, Diana Zhang, Swarun Kumar, and Stephanie Gil. 2022. “
Toolbox Release: A WiFi-Based Relative Bearing Framework for Robotics.” IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022.
AbstractThis paper presents the WiFi-Sensor-for-Robotics (WSR) open-source toolbox. It enables robots in a team to obtain relative bearing to each other, even in non-line-of-sight (NLOS) settings which is a very challenging problem in robotics. It does so by analyzing the phase of their communicated WiFi signals as the robots traverse the environment. This capability, based on the theory developed in our prior works, is made available for the first time as an open-source toolbox. It is motivated by the lack of easily deployable solutions that use robots' local resources (e.g WiFi) for sensing in NLOS. This has implications for multi-robot mapping and rendezvous, ad-hoc robot networks, and security in multi-robot teams, amongst other applications. The toolbox is designed for distributed and online deployment on robot platforms using commodity hardware and on-board sensors. We also release datasets demonstrating its performance in NLOS and line-of-sight (LOS) settings and for a multi-robot localization use case. Empirical results for hardware experiments show that the bearing estimation from our toolbox achieves accuracy with mean and standard deviation of 1.13 degrees, 11.07 degrees in LOS and 6.04 degrees, 26.4 degrees for NLOS, respectively, in an indoor office environment.
author_version.pdf