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\"Mobile Robotics offers comprehensive coverage of the essentials of the field suitable for both students and practitioners. Adapted from Alonzo Kelly's graduate and undergraduate courses, the content of the book reflects current approaches to developing effective mobile robots. Professor Kelly adapts principles and techniques from the fields of mathematics, physics and numerical methods to present a consistent framework in a notation that facilitates learning and highlights relationships between topics. This text was developed specifically to be accessible to senior level undergraduates in engineering and computer science, and includes supporting exercises to reinforce the lessons of each section. Practitioners will value Kelly's perspectives on practical applications of these principles. Complex subjects are reduced to implementable algorithms extracted from real systems wherever possible, to enhance the real-world relevance of the text\"-- Provided by publisher.

Off-road autonomous navigation is one of the most difficult automation challenges from the point of view of constraints on mobility, speed of motion, lack of environmental structure, density of hazards, and typical lack of prior information. This paper describes an autonomous navigation software system for outdoor vehicles which includes perception, mapping, obstacle detection and avoidance, and goal seeking. It has been used on several vehicle testbeds including autonomous HMMWV's and planetary rover prototypes. To date, it has achieved speeds of 15 km/hr and excursions of 15 km.We introduce algorithms for optimal processing and computational stabilization of range imagery for terrain mapping purposes. We formulate the problem of trajectory generation as one of predictive control searching trajectories expressed in command space. We also formulate the problem of goal arbitration in local autonomous mobility as an optimal control problem. We emphasize the modeling of vehicles in state space form. The resulting high fidelity models stabilize coordinated control of a high speed vehicle for both obstacle avoidance and goal seeking purposes. An intermediate predictive control layer is introduced between the typical high-level strategic or artificial intelligence layer and the typical low-level servo control layer. This layer incorporates some deliberation, and some environmental mapping as do deliberative AI planners, yet it also emphasizes the real-time aspects of the problem as do minimalist reactive architectures.

A basic requirement of autonomous vehicles is that of guaranteeing the safety of the vehicle by avoiding hazardous situations. This paper analyses this requirement in general terms of real-time response, throughput, and the resolution and accuracy of sensors and computations. Several nondimensional expressions emerge which characterize requirements in canonical form.The automatic generation of dense geometric models for autonomously navigating vehicles is a computationally expensive process. Using first principles, it is possible to quantify the relationship between the raw throughput required of the perception system and the maximum safely achievable speed of the vehicle. We derive several useful expressions for the complexity of terrain mapping perception under various assumptions. All of them can be reduced to polynomials in the response distance.The significant time consumed by geometric perception degrades real-time response characteristics. Using our results, several strategies of active geometric perception arise which are practical for autonomous vehicles and increasingly important at higher speeds.

Autonomous robot vehicles promise many ultimate civilian, military, and space applications. Off-road autonomous vehicles must engage the world exactly as they find it without relying on having it engineered to suit them. For this reason, off-road autonomous navigation is one of the most difficult automation challenges. Previous work in the area has been disappointing from the perspective of the speeds attained, and the inability of systems to travel long distances autonomously. Indeed, no system has travelled an autonomous mile or exceeded 3 m/s speeds. To date, no off-road system has approached the capabilities needed to address real applications. This thesis examines and proposes a solution to the problem of high speed autonomous navigation of outdoor vehicles. As a systems-level effort, aspects of perception, path planning, position estimation, and to a lesser extent, strategic planning and motion control are considered. The emphasis of the work has been to assess the fundamental requirements of the problem, and to validate the conclusions of this assessment through the demonstration of an improved ability to achieve a real cross-country mission on several vehicle testbeds. Results indicate that cross-country navigation systems of unprecedented capability are possible if they are designed to optimally utilize limited computing resources. A system of unprecedented performance has been constructed and extensively tested. The essential argument of the thesis is one of architecture. An intermediate intelligent predictive control layer is introduced between the typical high-level strategic or artificial intelligence layer and the typical low-level servo control layer. This new layer, the tactical layer, incorporates some deliberation, and some environmental mapping as do deliberative AI planners, yet it also emphasizes the real-time aspects of the problem as do minimalist reactive architectures. The contribution of the work is a codified systems theory that permits future design efforts to benefit from the experience and a fieldworthy prototype system that provides a baseline capability for continued research. Specific results include an analysis of the complexity of range image perception for autonomous vehicles and an associated computational image stabilization algorithm which permits highest vehicle speeds. The problem of local autonomous mobility has been formulated entirely in an optimal control context. In this context, the concepts of actuation space and hazard space replace the configuration space that is more typical of AI planners. The resulting high fidelity models stabilize coordinated control of a high speed vehicle for both obstacle avoidance and goal seeking purposes.

This software controls a rover platform to traverse rocky terrain autonomously, plan paths, and avoid obstacles using its stereo hazard and navigation cameras. It does so while continuously tracking a target of interest selected from 10 20 m away. The rover drives and tracks the target until it reaches the vicinity of the target. The rover then positions itself to approach the target, deploys its robotic arm, and places the end effector instrument on the designated target to within 2-3-cm accuracy of the originally selected target. This software features continuous navigation in a fairly rocky field in an outdoor environment and the ability to enable the rover to avoid large rocks and traverse over smaller ones. Using point-and-click mouse commands, a scientist designates targets in the initial imagery acquired from the rover s mast cameras. The navigation software uses stereo imaging, traversability analysis, path planning, trajectory generation, and trajectory execution. It also includes visual target tracking of a designated target selected from 10 m away while continuously navigating the rocky terrain. Improvements in this design include steering while driving, which uses continuous curvature paths. There are also several improvements to the traversability analyzer, including improved data fusion of traversability maps that result from pose estimation uncertainties, dealing with boundary effects to enable tighter maneuvers, and handling a wider range of obstacles. This work advances what has been previously developed and integrated on the Mars Exploration Rovers by using algorithms that are capable of traversing more rock-dense terrains, enabling tight, thread-the-needle maneuvers. These algorithms were integrated on the newly refurbished Athena Mars research rover, and were fielded in the JPL Mars Yard. Forty-three runs were conducted with targets at distances ranging from 5 to 15 m, and a success rate of 93% was achieved for placement of the instrument within 2-3 cm of the target.

The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2), an Earth-observing laser altimetry mission, is currently providing global elevation measurements. Geolocation validation confirms the altimeter’s ability to accurately position the measurement on the surface of the Earth and provides insight into the fidelity of the geolocation determination process. Surfaces well characterized by independent methods are well suited to provide a measure of the ICESat-2 geolocation accuracy through statistical comparison. This study compares airborne lidar data with the ICESat-2 along-track geolocated photon data product to determine the horizontal geolocation accuracy by minimizing the vertical residuals between datasets. At the same location arrays of corner cube retro-reflectors (CCRs) provide unique signal signatures back to the satellite from their known positions to give a deterministic solution of the laser footprint diameter and the geolocation accuracy for those cases where two or more CCRs were illuminated within one ICESat-2 transect. This passive method for diameter recovery and geolocation accuracy assessment is implemented at two locations: White Sands Missile Range (WSMR) in New Mexico and along the 88°S latitude line in Antarctica. This early on-orbit study provides results as a proof of concept for this passive validation technique. For the cases studied the diameter value ranged from 10.6 to 12 m. The variability is attributed to the statistical nature of photon-counting lidar technology and potentially, variations in the atmospheric conditions that impact signal transmission. The geolocation accuracy results from the CCR technique and airborne lidar comparisons are within the mission requirement of 6.5 m.

In species with internal fertilization, females can favour certain males over others, not only before mating but also within the female’s reproductive tract after mating. Here, we ask whether such directional post-mating (that is, cryptic) female mate choice can also occur in species with external fertilization. Using an in vitro sperm competition experiment, we demonstrate that female ovarian fluid (ovarian fluid) changes the outcome of sperm competition by decreasing the importance of sperm number thereby increasing the relative importance of sperm velocity. We further show that ovarian fluid does not differentially affect sperm from alternative male phenotypes, but generally enhances sperm velocity, motility, straightness and chemoattraction. Under natural conditions, female ovarian fluid likely increases the paternity of the preferred parental male phenotype, as these males release fewer but faster sperm. These results imply females have greater control over fertilization and potential to exert selection on males in species with external fertilization than previously thought possible. In some species with internal fertilization, females can mate with multiple males and then manipulate which sperm fertilize the eggs. Here, Alonzo et al. find that by releasing ovarian fluid along with their eggs, female ocellated wrasse are able to influence paternity despite having external fertilization.

In 2023, the US FDA approved several new cancer drugs and biologic agents, including seven small-molecule inhibitors, four bispecific T cell engagers, two anti-PD-1 antibodies and one cell therapy product. Regulatory focus areas included analyses of biomarker-positive subgroups that drive efficacy, equipoise in randomized controlled trials and a new authority to require confirmatory trials be underway before accelerated approval.

NASA launched its second Earth observing laser altimeter in 2018 with mission objectives of studying the changes in our climate by monitoring global elevations, particularly in the polar regions. Since the mission is focused on generating accurate elevations and elevation change, the geolocation (or geodetic position) of the measurements are of upmost importance to each of the scientific disciplines supported by these observations. Geolocation validation is required to ensure that the mission is meeting its objectives with the appropriate level of geolocation accuracy. One validation technique uses small optical reflectors placed in a specific pattern along one or more satellite ground-tracks. The optics provide a unique signal back to the satellite that can be used to compare the geolocation of these returns in the data to the known position on the surface. Results of the position comparison indicate the measurement locations are accurate to within 3.5 m with a standard deviation of 1.6 m. They also provide a method for determining a representative footprint diameter using geometric analysis, which resulted in an average value of 10.9 m +- 2.1 m.

Acinetobacter baumannii (named in honor of the American bacteriologists Paul and Linda Baumann) is a Gram-negative, multidrug-resistant (MDR) pathogen that causes nosocomial infections, especially in intensive care units (ICUs) and immunocompromised patients with central venous catheters. A. baumannii has developed a broad spectrum of antimicrobial resistance, associated with a higher mortality rate among infected patients compared with other non-baumannii species. In terms of clinical impact, resistant strains are associated with increases in both in-hospital length of stay and mortality. A. baumannii can cause a variety of infections; most involve the respiratory tract, especially ventilator-associated pneumonia, but bacteremia and skin wound infections have also been reported, the latter of which has been prominently observed in the context of war-related trauma. Cases of meningitis associated with A. baumannii have been documented. The most common risk factor for the acquisition of MDR A baumannii is previous antibiotic use, following by mechanical ventilation, length of ICU/hospital stay, severity of illness, and use of medical devices. Current efforts focus on addressing all the antimicrobial resistance mechanisms described in A. baumannii, with the objective of identifying the most promising therapeutic scheme. Bacteriophage- and artilysin-based therapeutic approaches have been described as effective, but further research into their clinical use is required