Index to NASA Tech Briefs, 1975

This index contains abstracts and four indexes--subject, personal author, originating Center, and Tech Brief number--for 1975 Tech Briefs

Application of advanced technology to space automation

Automated operations in space provide the key to optimized mission design and data acquisition at minimum cost for the future. The results of this study strongly accentuate this statement and should provide further incentive for immediate development of specific automtion technology as defined herein. Essential automation technology requirements were identified for future programs. The study was undertaken to address the future role of automation in the space program, the potential benefits to be derived, and the technology efforts that should be directed toward obtaining these benefits

Lateral guidance of all-wheel steered multiple-articulated vehicles

Nowadays, roads are becoming more and more congested, resulting in increasing economic losses due to delays. One way to solve this problem is to persuade people use public transp ortation more frequently. To achieve this, public transportation has to be improved. One way to improve public transportation is to construct a new kind of vehicle that combines the advantages of both commuter busses and railroad vehicles. Such a vehicle could be an all-wheel steered multiple-articulated vehicle. In the city of Eindhoven in The Netherlands, a new kind of transportation system will be operational in the year 2003, which is based on such vehicles. To achieve a track following behaviour similar to railroad vehicles, these vehicles have to be equipped with a lateral guidance system for steering them along a prede¯ne d path. This thesis deals with the design of such a guidance system. To achieve good tracking performance, the guidance system has to be modelbased. Therefore, a dynamic vehicle model has been derived. This model describes the nonlinear planar dynamics of an all-wheel steered n-carriage multiple-articulated vehicle. To validate this model, its frequency responses have been compared with the frequency responses of a 125 degrees of freedom multi-body model. This comparison shows good performance between both models. In order to show that a dynamic vehicle model is required, a comparison has been made between the dynamic vehicle model and a model describing only the kinematics of the vehicle. The position of the vehicle with respect to the path to be followed is crucial for proper control and therefore a measurement method based on the utilization of rotation symmetric bar magnets is presented. These magnets are buried in the road. By utilizing the rotation symmetry, the position to the magnet can be determined independently of the measurement height and the strength of the magnet. One requirement is that two ¯eld components are measured. It is shown that the sensitivity of this method to slant of the magnet and/or vehicle can be reduced by using a second dual-axes ¯eld sensor instead of one. Validation measurements show that the distance to the magnet can be determined with about 2 cm accuracy with 90 slant of the magnet. The permanent magnets yield position information exclusively and only at discrete instances. For controller design, knowledge of the complete state of the vehicle is desirable. An extended Kalman ¯lter has been designed to obtain continuous estimates of this state. To keep the in°uence of varying vehicle parameters small, accelerometers are used as input of the Kalman ¯lter. The accelerometer o®sets, road banking angle and vehicle roll angle are estimated online, to reduce the e®ect of these parameters. The position information obtained from the permanent magnets is used to apply corrections to state predictions that are based on the accelerometer outputs. The discrete and asynchronous character of this position information has been dealt with by implementing the Kalman ¯lter in a multi-rate fashion. Articulation angle sensors, wheel encoders and rate gyros are added to the Kalman ¯lter to improve the performance and to obtain redundan cy of sensors. The vehicles that will be used in the public transportation system in Eindhoven have, apart from all-wheel steering, also independent electrical drives on each of the wheels, except the two wheels at the front. This independent drive can in principle also be used for steering the vehicle, by using the drives on one axle in a di®erential way. A singular value analysis shows that steering with normal steering angles is much more in°uential than using these di®erential torques. It has also been analyzed that both at low and high speed all-wheel steering is bene¯cial to reduce o®-tracking of the rear axles and to improve the yaw dynamics of the vehicle. Two di®erent controllers have been designed for steering the vehicle, based on the outputs of the Kalman ¯lter, along the path to be followed. The ¯rst of these controllers is a feedback linearizing controller. This controller can be considered to consist of two control loops. The inner loop linearizes the planar vehicle dynamics, under the assumption that the steering system dynamics can be neglected. The outer control loop is used to counteract parameter uncertainty and disturbances. For this outer loop, a PID controller has been used. The second controller is a so-called backsteppin g controller. With this controller, also the steering actuator dynamics are taken into account. To simulate the behavior of the lateral guidance system, a more complex vehicle model has been used. This model describes besides the planar vehicle dynamics also the dynamics of the susp ension system. A nonlinear tire model has been used in this model. Simulations with this 3D simulation model show good tracking performance for both the feedback linearizing controller and the backstepping controller. The backstepping controller shows improved tracking performance compared to the feedback linearizing controller. However, this goes at the cost of increased high frequent behavior or the lateral acceleration

Lateral guidance of all-wheel steered multiple-articulated vehicles

Nowadays, roads are becoming more and more congested, resulting in increasing economic losses due to delays. One way to solve this problem is to persuade people use public transp ortation more frequently. To achieve this, public transportation has to be improved. One way to improve public transportation is to construct a new kind of vehicle that combines the advantages of both commuter busses and railroad vehicles. Such a vehicle could be an all-wheel steered multiple-articulated vehicle. In the city of Eindhoven in The Netherlands, a new kind of transportation system will be operational in the year 2003, which is based on such vehicles. To achieve a track following behaviour similar to railroad vehicles, these vehicles have to be equipped with a lateral guidance system for steering them along a prede¯ne d path. This thesis deals with the design of such a guidance system. To achieve good tracking performance, the guidance system has to be modelbased. Therefore, a dynamic vehicle model has been derived. This model describes the nonlinear planar dynamics of an all-wheel steered n-carriage multiple-articulated vehicle. To validate this model, its frequency responses have been compared with the frequency responses of a 125 degrees of freedom multi-body model. This comparison shows good performance between both models. In order to show that a dynamic vehicle model is required, a comparison has been made between the dynamic vehicle model and a model describing only the kinematics of the vehicle. The position of the vehicle with respect to the path to be followed is crucial for proper control and therefore a measurement method based on the utilization of rotation symmetric bar magnets is presented. These magnets are buried in the road. By utilizing the rotation symmetry, the position to the magnet can be determined independently of the measurement height and the strength of the magnet. One requirement is that two ¯eld components are measured. It is shown that the sensitivity of this method to slant of the magnet and/or vehicle can be reduced by using a second dual-axes ¯eld sensor instead of one. Validation measurements show that the distance to the magnet can be determined with about 2 cm accuracy with 90 slant of the magnet. The permanent magnets yield position information exclusively and only at discrete instances. For controller design, knowledge of the complete state of the vehicle is desirable. An extended Kalman ¯lter has been designed to obtain continuous estimates of this state. To keep the in°uence of varying vehicle parameters small, accelerometers are used as input of the Kalman ¯lter. The accelerometer o®sets, road banking angle and vehicle roll angle are estimated online, to reduce the e®ect of these parameters. The position information obtained from the permanent magnets is used to apply corrections to state predictions that are based on the accelerometer outputs. The discrete and asynchronous character of this position information has been dealt with by implementing the Kalman ¯lter in a multi-rate fashion. Articulation angle sensors, wheel encoders and rate gyros are added to the Kalman ¯lter to improve the performance and to obtain redundan cy of sensors. The vehicles that will be used in the public transportation system in Eindhoven have, apart from all-wheel steering, also independent electrical drives on each of the wheels, except the two wheels at the front. This independent drive can in principle also be used for steering the vehicle, by using the drives on one axle in a di®erential way. A singular value analysis shows that steering with normal steering angles is much more in°uential than using these di®erential torques. It has also been analyzed that both at low and high speed all-wheel steering is bene¯cial to reduce o®-tracking of the rear axles and to improve the yaw dynamics of the vehicle. Two di®erent controllers have been designed for steering the vehicle, based on the outputs of the Kalman ¯lter, along the path to be followed. The ¯rst of these controllers is a feedback linearizing controller. This controller can be considered to consist of two control loops. The inner loop linearizes the planar vehicle dynamics, under the assumption that the steering system dynamics can be neglected. The outer control loop is used to counteract parameter uncertainty and disturbances. For this outer loop, a PID controller has been used. The second controller is a so-called backsteppin g controller. With this controller, also the steering actuator dynamics are taken into account. To simulate the behavior of the lateral guidance system, a more complex vehicle model has been used. This model describes besides the planar vehicle dynamics also the dynamics of the susp ension system. A nonlinear tire model has been used in this model. Simulations with this 3D simulation model show good tracking performance for both the feedback linearizing controller and the backstepping controller. The backstepping controller shows improved tracking performance compared to the feedback linearizing controller. However, this goes at the cost of increased high frequent behavior or the lateral acceleration

Aerospace Medicine and Biology. A continuing bibliography with indexes

This bibliography lists 244 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1981. Aerospace medicine and aerobiology topics are included. Listings for physiological factors, astronaut performance, control theory, artificial intelligence, and cybernetics are included

Vehicle Localization Kalman Filtering for Traffic Light Advisor Application in Urban Scenarios

The recent advancements in Intelligent Transportation Systems (ITS) have revealed significant potential for enhancing traffic management through Advanced Driver Assist Systems (ADASs), with benefits for both safety and environment. This research paper proposes a vehicle localization technique based on Kalman filtering, as accurate positioning of the ego-vehicle is essential for the proper functioning of the Traffic Light Advisor (TLA) system. The aim of the TLA is to calculate the most suitable speed to safely reach and pass the first traffic light in front of the vehicle and subsequently keep that velocity constant to overcome the following traffic light, thus allowing safer and more efficient driving practices, thereby reducing safety risks, and minimizing energy consumption. To overcome Global Positioning Systems (GPS) limitations encountered in urban scenarios, a multi-rate sensor fusion approach based on the Kalman filter with map matching and a simple kinematic one-dimensional model is proposed. The experimental results demonstrate an estimation error below 0.5 m on urban roads with GPS signal loss areas, making it suitable for TLA application. The experimental validation of the Traffic Light Advisor system confirmed the expected benefits with a 40% decrease in energy consumption compared to unassisted driving

Contributions to automated realtime underwater navigation

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2012This dissertation presents three separate–but related–contributions to the art of underwater navigation. These methods may be used in postprocessing with a human in the loop, but the overarching goal is to enhance vehicle autonomy, so the emphasis is on automated approaches that can be used in realtime. The three research threads are: i) in situ navigation sensor alignment, ii) dead reckoning through the water column, and iii) model-driven delayed measurement fusion. Contributions to each of these areas have been demonstrated in simulation, with laboratory data, or in the field–some have been demonstrated in all three arenas. The solution to the in situ navigation sensor alignment problem is an asymptotically stable adaptive identifier formulated using rotors in Geometric Algebra. This identifier is applied to precisely estimate the unknown alignment between a gyrocompass and Doppler velocity log, with the goal of improving realtime dead reckoning navigation. Laboratory and field results show the identifier performs comparably to previously reported methods using rotation matrices, providing an alignment estimate that reduces the position residuals between dead reckoning and an external acoustic positioning system. The Geometric Algebra formulation also encourages a straightforward interpretation of the identifier as a proportional feedback regulator on the observable output error. Future applications of the identifier may include alignment between inertial, visual, and acoustic sensors. The ability to link the Global Positioning System at the surface to precision dead reckoning near the seafloor might enable new kinds of missions for autonomous underwater vehicles. This research introduces a method for dead reckoning through the water column using water current profile data collected by an onboard acoustic Doppler current profiler. Overlapping relative current profiles provide information to simultaneously estimate the vehicle velocity and local ocean current–the vehicle velocity is then integrated to estimate position. The method is applied to field data using online bin average, weighted least squares, and recursive least squares implementations. This demonstrates an autonomous navigation link between the surface and the seafloor without any dependence on a ship or external acoustic tracking systems. Finally, in many state estimation applications, delayed measurements present an interesting challenge. Underwater navigation is a particularly compelling case because of the relatively long delays inherent in all available position measurements. This research develops a flexible, model-driven approach to delayed measurement fusion in realtime Kalman filters. Using a priori estimates of delayed measurements as augmented states minimizes the computational cost of the delay treatment. Managing the augmented states with time-varying conditional process and measurement models ensures the approach works within the proven Kalman filter framework–without altering the filter structure or requiring any ad-hoc adjustments. The end result is a mathematically principled treatment of the delay that leads to more consistent estimates with lower error and uncertainty. Field results from dead reckoning aided by acoustic positioning systems demonstrate the applicability of this approach to real-world problems in underwater navigation.I have been financially supported by: the National Defense Science and Engineering Graduate (NDSEG) Fellowship administered by the American Society for Engineering Education, the Edwin A. Link Foundation Ocean Engineering and Instrumentation Fellowship, and WHOI Academic Programs office

Automotive sensor fusion systems for traffic aware adaptive cruise control

The autonomous driving (AD) industry is advancing at a rapid pace. New sensing technology for tracking vehicles, controlling vehicle behavior, and communicating with infrastructure are being added to commercial vehicles. These new automotive technologies reduce on road fatalities, improve ride quality, and improve vehicle fuel economy. This research explores two types of automotive sensor fusion systems: a novel radar/camera sensor fusion system using a long shortterm memory (LSTM) neural network (NN) to perform data fusion improving tracking capabilities in a simulated environment and a traditional radar/camera sensor fusion system that is deployed in Mississippi State’s entry in the EcoCAR Mobility Challenge (2019 Chevrolet Blazer) for an adaptive cruise control system (ACC) which functions in on-road applications. Along with vehicles, pedestrians, and cyclists, the sensor fusion system deployed in the 2019 Chevrolet Blazer uses vehicle-to-everything (V2X) communication to communicate with infrastructure such as traffic lights to optimize and autonomously control vehicle acceleration through a connected corrido

Predicting Trajectory Paths For Collision Avoidance Systems

This work was motivated by the idea of developing a more encompassing collision avoidance system that supported vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications. Current systems are mostly based on line of sight sensors that are used to prevent a collision, but these systems would prevent even more accidents if they could detect possible collisions before both vehicles were in line of sight. For this research we concentrated mostly on the aspect of improving the prediction of a vehicle\u27s future trajectory, particularly on non-straight paths. Having an accurate prediction of where the vehicle is heading is crucial for the system to reliably determine possible path intersections of more than one vehicle at the same time. We first evaluated the benefits of merging Global Positioning System (GPS) data with the Geographical Information System (GIS) data to correct improbable predicted positions. We then created a new algorithm called the Dead Reckoning with Dynamic Errors (DRWDE) sensor fusion, which can predict future positions at the rate of its fastest sensor, while improving the handling of accumulated error while some of the sensors are offline for a given period of time. The last part of out research consisted in the evaluation of the use of smartphones\u27 built-in sensors to predict a vehicle\u27s trajectory, as a possible intermediate solution for a vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications, until all vehicles have all the necessary sensors and communication infrastructure to fully populate this new system. For the first part of our research, the actual experimental results validated our proposed system, which reduced the position prediction errors during curves to around half of what it would be without the use of GIS data for prediction corrections. The next improvement we worked on was the ability to handle change in noise, depending on unavailable sensor measurements, permitting a flexibility to use any type of sensor and still have the system run at the fastest frequency available. Compared to a more common KF implementation that run at the rate of its slowest sensor (1Hz in our setup), our experimental results showed that our DRWDE (running at 10Hz) yielded more accurate predictions (25-50% improvement) during abrupt changes in the heading of the vehicle. The last part of our research showed that, comparing to results obtained with the vehicle-mounted sensors, some smartphones yield similar prediction errors and can be used to predict a future position

Stochastic Real-time Optimal Control: A Pseudospectral Approach for Bearing-Only Trajectory Optimization

A method is presented to couple and solve the optimal control and the optimal estimation problems simultaneously, allowing systems with bearing-only sensors to maneuver to obtain observability for relative navigation without unnecessarily detracting from a primary mission. A fundamentally new approach to trajectory optimization and the dual control problem is developed, constraining polynomial approximations of the Fisher Information Matrix to provide an information gradient and allow prescription of the level of future estimation certainty required for mission accomplishment. Disturbances, modeling deficiencies, and corrupted measurements are addressed with recursive updating of the target estimate with an Unscented Kalman Filter and the optimal path with Radau pseudospectral collocation methods and sequential quadratic programming. The basic real-time optimal control (RTOC) structure is investigated, specifically addressing limitations of current techniques in this area that lose error integration. The resulting guidance method can be applied to any bearing-only system, such as submarines using passive sonar, anti-radiation missiles, or small UAVs seeking to land on power lines for energy harvesting. Methods and tools required for implementation are developed, including variable calculation timing and tip-tail blending for potential discontinuities. Validation is accomplished with simulation and flight test, autonomously landing a quadrotor helicopter on a wire