Maxim A. Batalin

Coverage, Exploration and Deployment by a Mobile Robot and Communication Network

We consider the problem of coverage and exploration of an unknown dynamic environment using a mobile robot. The environment is assumed to be large enough such that constant motion by the robot is needed to cover the environment. We present an efficient minimalist algorithm which assumes that global information is not available (neither a map, nor GPS). Our algorithm deploys a network of radio beacons which assists the robot in coverage. The network is also used by the robot for navigation. The deployed network can also be used for applications other than coverage (such as multi-robot task allocation). Simulation experiments are presented which show the collaboration between the deployed network and mobile robot for the tasks of coverage/exploration, network deployment and maintenance (repair), and mobile robot recovery (homing behavior). We discuss a theoretical basis for our algorithm on graphs and show the results of the simulated scenario experiments.

Mobile robot navigation using a sensor network

We describe an algorithm for robot navigation using a sensor network embedded in the environment. Sensor nodes act as signposts for the robot to follow, thus obviating the need for a map or localization on the part of the robot. Navigation directions are computed within the network (not on the robot) using value iteration. Using small low-power radios, the robot communicates with nodes in the network locally, and makes navigation decisions based on which node it is near. An algorithm based on processing of radio signal strength data was developed so the robot could successfully decide which node neighborhood it belonged to. Extensive experiments with a robot and a sensor network confirm the validity of the approach.

Spreading Out: A Local Approach to Multi-robot Coverage

The problem of coverage without a priori global information about the environment is a key element of the general exploration problem. Applications vary from exploration of the Mars surface to the urban search and rescue (USAR) domain, where neither a map, nor a Global Positioning System (GPS) are available. We propose two algorithms for solving the 2D coverage problem using multiple mobile robots. The basic premise of both algorithms is that local dispersion is a natural way to achieve global coverage. Thus, both algorithms are based on local, mutually dispersive interaction between robots when they are within sensing range of each other. Simulations show that the proposed algorithms solve the problem to within 5–7% of the (manually generated) optimal solutions. We show that the nature of the interaction needed between robots is very simple; indeed anonymous interaction slightly outperforms a more complicated local technique based on ephemeral identification.

Call and response: experiments in sampling the environment

Monitoring of environmental phenomena with embedded networked sensing confronts the challenges of both unpredictable variability in the spatial distribution of phenomena, coupled with demands for a high spatial sampling rate in three dimensions. For example, low distortion mapping of critical solar radiation properties in forest environments may require two-dimensional spatial sampling rates of greater than 10 samples/m2 over transects exceeding 1000 m2. Clearly, adequate sampling coverage of such a transect requires an impractically large number of sensing nodes. This paper describes a new approach where the deployment of a combination of autonomous-articulated and static sensor nodes enables sufficient spatiotemporal sampling densityo ver large transects to meet a general set of environmental mapping demands. To achieve this we have developed an embedded networked sensor architecture that merges sensing and articulation with adaptive algorithms that are responsive to both variabilityin environmental phenomena discovered bythe mobile sensors and to discrete events discovered byst atic sensors. We begin byde scribing the class of important driving applications, the statistical foundations for this new approach, and task allocation. We then describe our experimental implementation of adaptive, event aware, exploration algorithms, which exploit our wireless, articulated sensors operating with deterministic motion over large areas. Results of experimental measurements and the relationship among sampling methods, event arrival rate, and sampling performance are presented.

MEDIC: Medical embedded device for individualized care

OBJECTIVE Presented work highlights the development and initial validation of a medical embedded device for individualized care (MEDIC), which is based on a novel software architecture, enabling sensor management and disease prediction capabilities, and commercially available microelectronic components, sensors and conventional personal digital assistant (PDA) (or a cell phone). METHODS AND MATERIALS In this paper, we present a general architecture for a wearable sensor system that can be customized to an individual patients needs. This architecture is based on embedded artificial intelligence that permits autonomous operation, sensor management and inference, and may be applied to a general purpose wearable medical diagnostics. RESULTS A prototype of the system has been developed based on a standard PDA and wireless sensor nodes equipped with commercially available Bluetooth radio components, permitting real-time streaming of high-bandwidth data from various physiological and contextual sensors. We also present the results of abnormal gait diagnosis using the complete system from our evaluation, and illustrate how the wearable system and its operation can be remotely configured and managed by either enterprise systems or medical personnel at centralized locations. CONCLUSION By using commercially available hardware components and software architecture presented in this paper, the MEDIC system can be rapidly configured, providing medical researchers with broadband sensor data from remote patients and platform access to best adapt operation for diagnostic operation objectives.

Networked infomechanical systems: a mobile embedded networked sensor platform

Networked infomechanical systems (NIMS) introduces a new actuation capability for embedded networked sensing. By exploiting a constrained actuation method based on rapidly deployable infrastructure, NIMS suspends a network of wireless mobile and fixed sensor nodes in three-dimensional space. This permits run-time adaptation with variable sensing location, perspective, and even sensor type. Discoveries in NIMS environmental investigations have raised requirements for 1) new embedded platforms integrating many diverse sensors with actuators, and 2) advances for in-network sensor data processing. This is addressed with a new and generally applicable processor-preprocessor architecture described in this paper. Also this paper describes the successful integration of R, a powerful statistical computing environment, into the embedded NIMS node platform.

Reliability and Validity of Bilateral Ankle Accelerometer Algorithms for Activity Recognition and Walking Speed After Stroke

Background and Purpose— Outcome measures of mobility for large stroke trials are limited to timed walks for short distances in a laboratory, step counters and ordinal scales of disability and quality of life. Continuous monitoring and outcome measurements of the type and quantity of activity in the community would provide direct data about daily performance, including compliance with exercise and skills practice during routine care and clinical trials. Methods— Twelve adults with impaired ambulation from hemiparetic stroke and 6 healthy controls wore triaxial accelerometers on their ankles. Walking speed for repeated outdoor walks was determined by machine-learning algorithms and compared to a stopwatch calculation of speed for distances not known to the algorithm. The reliability of recognizing walking, exercise, and cycling by the algorithms was compared to activity logs. Results— A high correlation was found between stopwatch-measured outdoor walking speed and algorithm-calculated speed (Pearson coefficient, 0.98; P=0.001) and for repeated measures of algorithm-derived walking speed (P=0.01). Bouts of walking >5 steps, variations in walking speed, cycling, stair climbing, and leg exercises were correctly identified during a day in the community. Compared to healthy subjects, those with stroke were, as expected, more sedentary and slower, and their gait revealed high paretic-to-unaffected leg swing ratios. Conclusions— Test–retest reliability and concurrent and construct validity are high for activity pattern-recognition Bayesian algorithms developed from inertial sensors. This ratio scale data can provide real-world monitoring and outcome measurements of lower extremity activities and walking speed for stroke and rehabilitation studies.

Efficient exploration without localization

We study the problem of exploring an unknown environment using a single robot. The environment is large enough (and possibly dynamic) that constant motion by the robot is needed to cover the environment. We term this the dynamic coverage problem. We present an efficient minimalist algorithm which assumes that global information is not available to the robot (neither a map, nor GPS). Our algorithm uses markers which the robot drops off as signposts to aid exploration. We conjecture that our algorithm has a cover time better than O(n log n), where the n markers that are deployed form the vertices of a regular graph. We provide experimental evidence in support of this conjecture. We show empirically that the performance of our algorithm on graphs is similar to its performance in simulation.

The SmartCane system: an assistive device for geriatrics

Falls are currently a leading cause of death from injury in the elderly. The usage of the conventional assistive cane devices is critical in reducing the risk of falls and is relied upon by over 4 million patients in the U.S.. While canes provide physical support as well as supplementary sensing feedback to patients, at the same time, these conventional aids also exhibit serious adverse effects that contribute to falls. The falls due to the improper usage of the canes are particularly acute in the elderly and disabled where reduced cognitive capacity accompanied by the burden of managing cane motion leads to increased risk. This paper describes the development of the SmartCane assistive system that encompasses broad engineering challenges that will impact general development of individualized, robust assistive and prosthetic devices. The SmartCane system combines advances in signal processing, embedded computing, and wireless networking technology to provide capabilities for remote monitoring, local signal processing, and real-time feedback on the cane usage. This system aims to reduce risks of injuries and falls by enabling training and guidance of patients in proper usage of assistive devices.

The Analysis of an Efficient Algorithm for Robot Coverage and Exploration based on Sensor Network Deployment

In this paper we present the design and theoretical analysis of a novel algorithm (LRV) that efficiently solves the problems of coverage, exploration and sensor network deployment at the same time. The basic premise behind the algorithm is that the robot carries network nodes as a payload, and in the process of moving around, emplaces the nodes into the environment based on certain local criteria. In turn, the nodes emit navigation directions for the robot as it goes by. Nodes recommend directions least recently visited by the robot, hence the name LRV. We formally establish the following two properties: 1. LRV is complete on graphs, and 2. LRV is optimal on trees. We present some experimental conjectures for LRV on regular square lattice graphs and compare its performance empirically to other graph exploration algorithms.