Abstract-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.

Abstract. 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.

Sudden cardiac deaths experienced by firefighters in the line of duty account for the largest proportion of deaths annually. Several fire service standards for fitness and wellness have been recommended but currently only 30% of U.S. fire departments are implementing programs for this purpose. The Department of Homeland Security Science and Technology Directorate has initiated the Physiological Health Assessment System for Emergency Responders (PHASER) program aiming to reduce these line-of-duty deaths through an integration of medical science and sensor technologies. Confirming previous reports, PHASER comprehensive risk assessment has identified lack of physical fitness with propensity for overexertion as a major modifiable risk factor. We sought to determine if current levels of fitness and cardiovascular disease (CVD) risk factors in a contemporary cohort of firefighters were better than those reported over the past 30 years. Fifty-one firefighters from a Southern California department were characterized for physical fitness and CVD risk factors using standard measures. Overall, physical fitness and risk factors were not different from previous reports of firefighter fitness and most subjects did not achieve recommended fitness standards. Considering the lack of widespread implementation of wellness/fitness programs in the U.S. fire service together with our findings that low physical fitness and the presence of CVD risk factors persist, we issue a call to action among health and fitness professionals to assist the fire service in implementing programs for firefighters that improve fitness and reduce CVD risk factors. Fitness professionals should be empowered to work with fire departments lending their expertise to guide programs that achieve these objectives, which may then lead to reduced incidence of sudden cardiac death or stroke.

Abstract. We consider the problem of coverage and exploration of an unknown dynamic environment using a mobile robot(s). The environment is assumed to be large enough such that constant motion by the robot(s) 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(s) in coverage. This network is also used for navigation. The deployed network can also be used for applications other than coverage. Simulation experiments are presented which show the collaboration between the deployed network and mobile robot(s) for the tasks of coverage/exploration, network deployment and maintenance (repair), and mobile robot(s) recovery (homing behavior). We present a theoretical basis for our algorithm on graphs and show the results of the simulated scenario experiments.

Abstract-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.

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.

Abstract. We consider the problem of coverage and exploration of an unknown dynamic environment using a mobile robot(s). The environment is assumed to be large enough such that constant motion by the robot(s) 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(s) in coverage. This network is also used for navigation. The deployed network can also be used for applications other than coverage. Simulation experiments are presented which show the collaboration between the deployed network and mobile robot(s) for the tasks of coverage/exploration, network deployment and maintenance (repair), and mobile robot(s) recovery (homing behavior). We present a theoretical basis for our algorithm on graphs and show the results of the simulated scenario experiments.

Abstract-We present the design and theoretical analysis of a novel algorithm termed least recently visited (LRV). LRV efficiently and simultaneously solves the problems of coverage, exploration, and sensor network deployment. The basic premise behind the algorithm is that a 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 experimental conjectures for LRV on regular square and cube lattice graphs and compare its performance empirically to other graph exploration algorithms. We study the effects of the order of the exploration and show on a square lattice that with an appropriately chosen order, LRV performs optimally. Finally, we discuss the implementation of LRV in simulation and in real hardware.