Jonathan Fink

Robust Control of Mobility and Communications in Autonomous Robot Teams

A team of robots are deployed to accomplish a task while maintaining a viable ad-hoc network capable of supporting data transmissions necessary for task fulfillment. Solving this problem necessitates: 1) estimation of the wireless propagation environment to identify viable point-to-point communication links; 2) determination of end-to-end routes to support data traffic; and 3) motion control algorithms to navigate through spatial configurations that guarantee required minimum levels of service. Therefore, we present methods for: 1) estimation of point-to-point channels using pathloss and spatial Gaussian process models; 2) data routing so as to determine suitable end-to-end communication routes given estimates of point-to-point channel rates; and 3) motion planning to determine robot trajectories restricted to configurations that ensure survival of the communication network. Because of the inherent uncertainty of wireless channels, the model of links and routes is stochastic. The criteria for route selection is to maximize the probability of network survival-defined as the ability to support target communication rates-given achievable rates on local point-to-point links. Maximum survival probability routes for present and future positions are input into a mobility control module that determines robot trajectories restricted to configurations that ensure the probability of network survival stays above a minimum reliability level. Local trajectory planning is proposed for simple environments and global planning is proposed for complex surroundings. The three proposed components are integrated and tested in experiments run in two different environments. Experimental results show successful navigation with continuous end-to-end connectivity.

RSS gradient-assisted frontier exploration and radio source localization

We consider the combined problem of frontier exploration in a complex indoor environment while seeking a radio source. To do this in an efficient manner, we incorporate radio signal strength (RSS) information into the exploration algorithm by locally sampling the RSS and estimating the 2-D RSS gradient. The algorithm exploits the local motion to collect RSS samples for gradient estimation and seeks to explore in a way that brings the robot to the signal source. This strategy avoids random or exhaustive exploration. An indoor experiment demonstrates the exploration algorithm that uses this information to dynamically prioritize candidate frontiers and traverse to a radio source. Simulations, including radio propagation modeling with a ray-tracing algorithm, enable study of control algorithm tradeoffs and statistical performance.

Motion planning for robust wireless networking

We propose an architecture and algorithms for maintaining end-to-end network connectivity for autonomous teams of robots. By adopting stochastic models of point-to-point wireless communication and computing robust solutions to the network routing problem, we ensure reliable connectivity during robot movement in complex environments. We fully integrate the solution to network routing with the choice of node positions through the use of randomized motion planning techniques. Experiments demonstrate that our method succeeds in navigating a complex environment while ensuring that end-to-end communication rates meet or exceed prescribed values within a target failure tolerance.

Short-Range Low-VHF Channel Characterization in Cluttered Environments

The lower VHF band has potential for low-power, short-range communications, as well as for geolocation applications, in both indoor and urban environments. Most prior work at low VHF focuses on longer range path loss modeling, often with one node elevated. In this paper, we study indoor/outdoor near-ground scenarios through experiments and electromagnetic wave propagation simulations. These include the effects of indoor penetration through walls and obstacles, as well as indoor/outdoor cases, for both line of sight (LoS) and nonLoS (NLoS), at ranges up to 200 m. Mounting our receiver (Rx) on a robotic platform enabled the collection of thousands of measurements over an extended indoor/outdoor test area. We measure the channel transfer function, employing bandpass waveform sampling, with pulse and tone probe signals. Based on statistical tests, we show that the measured channels have a nearly ideal scalar attenuation and delay transfer function, with minimal phase distortion, and little to no evidence of multipath propagation. Compared with higher VHF and above, the measured short-range VHF channels do not exhibit small-scale fading, which simplifies communications Rx signal processing, and enables phase-based geolocation techniques.

Efficient base station connectivity area discovery

Many applications of autonomy are significantly complicated by the need for wireless networking, with challenges including scalability and robustness. Radio accomplishes this in a complex environment, but suffers from rapid signal strength variation and attenuation typically much worse than free space loss. In this paper, we propose and test algorithms to autonomously discover the connectivity area for a base station in an unknown environment using an average of received signal strength (RSS) values and a RSS threshold to delineate the goodness of the channel. We combine region decomposition and RSS sampling to cast the problem as an efficient graph search. The nominal RSS in a sampling region is obtained by averaging local RSS samples to reduce the small-scale fading variation. The RSS gradient is exploited during exploration to develop an efficient approach for discovery of the base station connectivity boundary in an unknown environment. Indoor and outdoor experiments demonstrate the proposed techniques. The results can be used for sensing and collaborative autonomy, building base station coverage maps in unknown environments, and facilitating multi-hop relaying to a base station.

Application of Multi-Robot Systems to Disaster-Relief Scenarios with Limited Communication

In this systems description paper, we present a multi-robot solution for intelligence-gathering tasks in disaster-relief scenarios where communication quality is uncertain. First, we propose a formal problem statement in the context of operations research. The hardware configuration of two heterogeneous robotic platforms capable of performing experiments in a relevant field environment and a suite of autonomy-enabled behaviors that support operation in a communication-limited setting are described. We also highlight a custom user interface designed specifically for task allocation amongst a group of robots towards completing a central mission. Finally, we provide an experimental design and extensive, preliminary results for studying the effectiveness of our system.

Mapping with a ground robot in GPS denied and degraded environments

A robot system operating in an unknown environment must be able to track its position to perform its mission. Vehicles with a consistent view of the sky, e.g., aerial or water surface platforms, can reliably make use of GPS signals to correct accumulated error from inertial measurements and feature-based mapping techniques. However, ground robots that must operate across a wide range of environments suffer from additional constraints which degrade the performance of GPS such as multipath and occlusion. In this paper, we present a methodology for incorporating GPS measurements into a feature-based mapping system for two purposes: providing geo-referenced coordinates for high-level mission execution and correcting accumulated map error over long-term operation. We will present both the underlying system and experimental results from a variety of relevant environments such as military training facilities and large-scale mixed indoor and outdoor environments.

Online learning for characterizing unknown environments in ground robotic vehicle models

In pursuit of increasing the operational tempo of a ground robotics platform in unknown domains, we consider the problem of predicting the distribution of structural state-estimation error due to poorly-modeled platform dynamics as well as environmental effects. Such predictions are a critical component of any modern control approach that utilizes uncertainty information to provide robustness in control design. We use an online learning algorithm based on matrix factorization techniques to fit a statistical model of error that provides enough expressive power to enable prediction directly from motion control signals and low-level visual features. Moreover, we empirically demonstrate that this technique compares favorably to predictors that do not incorporate this information.

Hybrid architecture for communication-aware multi-robot systems

In this paper we propose a hybrid architecture that allows a team of mobile robots to self-organize into a multi-hop ad-hoc network and solve the joint mobility and communication problem in complex environments to complete a given task. The system consists of an outer global planning loop and an inner local loop responsible for motion and network routing, arranged in a two-stage feedback system. This system is able to leverage the benefits of previous systems, while avoiding their drawbacks. This results in a lightweight responsive system that is able to operate in complex environments with minimal global coordination while maintaining a minimum end-to-end data rate between robots. Two main benefits of our approach are demonstrated through experimentation superior performance over existing systems and dynamic adjustment to unexpected events. We conclude with a demonstration of the system operating in a realistic scenario, in which the team patrols a set of hallways.

Experimental analysis of models for trajectory generation on tracked vehicles

We begin to bridge the gap between high-level motion planning and execution by adopting models to abstract the complicated skid-steer vehicle dynamics and evaluating their suitability as motion predictors for a feed-forward control framework. We consider three kinematic motion models and a drivetrain model in experiments on two surface types with a small tracked vehicle. We perform statistical analysis of the predictive accuracy of these models when used to create optimal open-loop plans for a set of canonical maneuvers and discuss the applicability of these models for a closed-loop control framework.