A novel low-cost autonomous 3D LIDAR system

Thesis (M.S.) University of Alaska Fairbanks, 2018To aid in humanity's efforts to colonize alien worlds, NASA's Robotic Mining Competition pits universities against one another to design autonomous mining robots that can extract the materials necessary for producing oxygen, water, fuel, and infrastructure. To mine autonomously on the uneven terrain, the robot must be able to produce a 3D map of its surroundings and navigate around obstacles. However, sensors that can be used for 3D mapping are typically expensive, have high computational requirements, and/or are designed primarily for indoor use. This thesis describes the creation of a novel low-cost 3D mapping system utilizing a pair of rotating LIDAR sensors, attached to a mobile testing platform. Also, the use of this system for 3D obstacle detection and navigation is shown. Finally, the use of deep learning to improve the scanning efficiency of the sensors is investigated.Chapter 1. Introduction -- 1.1. Purpose -- 1.2. 3D sensors -- 1.2.1. Cameras -- 1.2.2. RGB-D Cameras -- 1.2.3. LIDAR -- 1.3. Overview of Work and Contributions -- 1.4. Multi-LIDAR and Rotating LIDAR Systems -- 1.5. Thesis Organization. Chapter 2. Hardware -- 2.1. Overview -- 2.2. Components -- 2.2.1. Revo Laser Distance Sensor -- 2.2.2. Dynamixel AX-12A Smart Serial Servo -- 2.2.3. Bosch BNO055 Inertial Measurement Unit -- 2.2.4. STM32F767ZI Microcontroller and LIDAR Interface Boards -- 2.2.5. Create 2 Programmable Mobile Robotic Platform -- 2.2.6. Acer C720 Chromebook and Genius Webcam -- 2.3. System Assembly -- 2.3.1. 3D LIDAR Module -- 2.3.2. Full Assembly. Chapter 3. Software -- 3.1. Robot Operating System -- 3.2. Frames of Reference -- 3.3. System Overview -- 3.4. Microcontroller Firmware -- 3.5. PC-Side Point Cloud Fusion -- 3.6. Localization System -- 3.6.1. Fusion of Wheel Odometry and IMU Data -- 3.6.2. ArUco Marker Localization -- 3.6.3. ROS Navigation Stack: Overview & Configuration -- 3.6.3.1. Costmaps -- 3.6.3.2. Path Planners. Chapter 4. System Performance -- 4.1. VS-LIDAR Characteristics -- 4.2. Odometry Tests -- 4.3. Stochastic Scan Dithering -- 4.4. Obstacle Detection Test -- 4.5. Navigation Tests -- 4.6. Detection of Black Obstacles -- 4.7. Performance in Sunlit Environments -- 4.8. Distance Measurement Comparison. Chapter 5. Case Study: Adaptive Scan Dithering -- 5.1. Introduction -- 5.2. Adaptive Scan Dithering Process Overview -- 5.3. Coverage Metrics -- 5.4. Reward Function -- 5.5. Network Configuration -- 5.6. Performance and Remarks. Chapter 6. Conclusions and Future Work -- 6.1. Conclusions -- 6.2. Future Work -- 6.3. Lessons Learned -- References

Experimental testbeds for real-time motion planning : implementation and lessons learned

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 103-107).A fundamental step in research on autonomous robotic systems is the actual development and test of experimental platforms, to validate the system design and the effective integration of hardware and real-time software. The objective of this thesis is to report on experimental implementation of platforms and testing environments for real-time motion planning. First of all, robust planning and control system using closed-loop prediction RRT approach was implemented on a robotic forklift. The system displayed robust performance in the execution of several tasks in an uncertain demonstration environment at Fort Belvoir in Virginia, in June, 2009. Second, an economical testbed based on an infrared motion capture system is implemented for indoors experiments. Exploiting the advantages of a controlled indoor environment and reliable navigation outputs through motion capture system, different variations of the planning problem can be explored with accuracy, safety, and convenience.(cont.) Additionally, a motion planning problem for a robotic vehicle whose dynamics depends on unknown parameters is introduced. Typically, the motion planning problems in robotics assume perfect knowledge of the robots' dynamics, and both planner and controller are responsible only for their own parts in hierarchical sense of the framework. A different approach is proposed here, in which the planner takes explicitly into account the uncertainties about the model parameters, and generates completely safe plans for the whole uncertain parameter range. As the vehicle executes the generated plan, the parameter uncertainty is decreased based on the observed behavior, and it gradually allows more efficient planning with smaller uncertainties.by Jeong hwan Jeon.S.M

Application of Game Theory to Interactive Lane Change Decision Making for Autonomous Driving

The decision-making and motion planning play a critical role in the autonomous driving by connecting the perception to the vehicle control. It aims at generating available paths in the specific driving environment considering vehicle safety and driving efficiency constraints as well as the ride comfort. The complexity of the decision-making depends on the target driving performances and the driving environment. The complexity of the future driving environment, due to the coexistence of automated and human-driven vehicles, makes the balance between safety, efficiency, and comfort much more challenging. Therefore, the focus of this research is to provide decision-making algorithms for an autonomous vehicle in the interactive driving environment where the surrounding vehicles are driven by human drivers who are unpredictable due to diverse driving behaviors. To tackle the above problem, tools from game theory are utilized to analyze the interactions between rational players. To consider the driver intentions of the surrounding vehicles, games with complete information and incomplete information are discussed. The driver behavior is learned during the driving process, based on the Gaussian Mixture Model (GMM) trained by the naturalistic driving data. Then the driver behavior of surrounding vehicles is transmitted to the incomplete information game model, so that the human preferences can be estimated and utilized by the ego vehicle to regulate the predictions of the driving environment. Based on the model of incomplete information game, the uncertainty and the variety of the surrounding human-driven vehicles are both focused. The driving decisions can be made adaptively according to the driving styles of the surrounding vehicles. The lane change scenario on a highway is selected as the research scene to test the performances of the proposed decision-making model. To make the simulation environment more realistic, the motions of the surrounding vehicles are modelled by the Intelligent Driver Model (IDM), whose driving styles are calibrated and classified in an explainable way based on the real driving data. Multiple scenarios are designed with driving style combinations of various surrounding vehicles. Moreover, the two-player game is extended to the multi-player game with the lateral behavior of the vehicles considered. Finally, the proposed model is validated by comparing the generated driving decisions and trajectories with human drivers’ driving profiles under the same driving conditions extracted from the naturalistic driving data. The results show that the driver aggressiveness estimation could help the ego vehicle change lane more efficiently and guarantee safety under the incomplete information model. The developed game-based decision-making model shows high potential to handle the uncertain interaction between the autonomous vehicle and human-driven vehicles

Ein neues Konzept für die Trajektoriengenerierung und -stabilisierung in zeitkritischen Verkehrsszenarien

Durch den Einsatz autonomer Fahrzeuge kann der Straßenverkehr effizienter, komfortabler und vor allem sicherer gestaltet werden. Neben der hierfür erforderlichen Umfeldwahrnehmung stellen besonders die Bewegungsplanung und -ausführung zeitkritischer Fahrmanöver zur Beherrschung von dynamischen Verkehrsszenarien eine große Herausforderung dar. Herkömmliche Verfahren, die trotz trickreicher Modifikationen dieser nicht gewachsen sind, werden konsequent durch trajektorienbasierte Konzepte ersetzt

Ein neues Konzept für die Trajektoriengenerierung und -stabilisierung in zeitkritischen Verkehrsszenarien

Durch den Einsatz autonomer Fahrzeuge kann der Straßenverkehr effizienter, komfortabler und vor allem sicherer gestaltet werden. Neben der hierfür erforderlichen Umfeldwahrnehmung stellen besonders die Bewegungsplanung und -ausführung zeitkritischer Fahrmanöver zur Beherrschung von dynamischen Verkehrsszenarien eine große Herausforderung dar. Herkömmliche Verfahren, die trotz trickreicher Modifikationen dieser nicht gewachsen sind, werden konsequent durch trajektorienbasierte Konzepte ersetzt

A practical approach to robotic design for the DARPA Urban Challenge

This article presents a practical approach to engineering a robot to effectively navigate in an urban environment. Inherent in this approach is the use of relatively simple sensors, actuators, and processors to generate robot vision, intelligence, and planning. Sensor data are fused from multiple low-cost, two-dimensional laser scanners With an innovative rotational mount to provide three-dimensional coverage with image processing using both range and intensity data. Information is combined With Doppler radar returns to yield a world view processed by a context-based reasoning control system to yield tactical mission commands forwarded to traditional proportional-integral-derivative (PID) control loops. As an example of simplicity and robustness, steering control successfully utilized a relatively simple follow-the-carrot guidance approach that has been successfully demonstrated at speeds of 60 mph (97 km/h). The approach yielded a robot that reached the finals of the Urban Challenge and completed approximately 2 h of the event before being forced to withdraw as a result of a global positioning system data failure. 0 2008 Wiley Periodicals, Inc

A practical approach to robotic design for the DARPA Urban Challenge

This article presents a practical approach to engineering a robot to effectively navigate in an urban environment. Inherent in this approach is the use of relatively simple sensors, actuators, and processors to generate robot vision, intelligence, and planning. Sensor data are fused from multiple low-cost, two-dimensional laser scanners With an innovative rotational mount to provide three-dimensional coverage with image processing using both range and intensity data. Information is combined With Doppler radar returns to yield a world view processed by a context-based reasoning control system to yield tactical mission commands forwarded to traditional proportional-integral-derivative (PID) control loops. As an example of simplicity and robustness, steering control successfully utilized a relatively simple follow-the-carrot guidance approach that has been successfully demonstrated at speeds of 60 mph (97 km/h). The approach yielded a robot that reached the finals of the Urban Challenge and completed approximately 2 h of the event before being forced to withdraw as a result of a global positioning system data failure. © 2008 Wiley Periodicals, Inc

A Practical Approach To Robotic Design For The Darpa Urban Challenge

This article presents a practical approach to engineering a robot to effectively navigate in an urban environment. Inherent in this approach is the use of relatively simple sensors, actuators, and processors to generate robot vision, intelligence, and planning. Sensor data are fused from multiple low-cost, two-dimensional laser scanners with an innovative rotational mount to provide three-dimensional coverage with image processing using both range and intensity data. Information is combined with Doppler radar returns to yield a world view processed by a context-based reasoning control system to yield tactical mission commands forwarded to traditional proportional-integral-derivative (PID) control loops. As an example of simplicity and robustness, steering control successfully utilized a relatively simple follow-the-carrot guidance approach that has been successfully demonstrated at speeds of 60 mph (97 km/h). The approach yielded a robot that reached the finals of the Urban Challenge and completed approximately 2 h of the event before being forced to withdraw as a result of a global positioning system data failure. © 2008 Wiley Periodicals, Inc