Decentralized path planning for multiple agents in complex environments using rapidly-exploring random trees

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 89-94).This thesis presents a novel approach to address the challenge of planning paths for real-world multi-agent systems operating in complex environments. The technique developed, the Decentralized Multi-Agent Rapidly-exploring Random Tree (DMARRT) algorithm, is an extension of the CL-RRT algorithm to the multi-agent case, retaining its ability to plan quickly even with complex constraints. Moreover, a merit-based token passing coordination strategy is also presented as a core component of the DMA-RRT algorithm. This coordination strategy makes use of the tree of feasible trajectories grown in the CL-RRT algorithm to dynamically update the order in which agents plan. This reordering is based on a measure of each agent's incentive to replan and allows agents with a greater incentive to plan sooner, thus reducing the global cost and improving the team's overall performance. An extended version of the algorithm, Cooperative DMA-RRT, is also presented to introduce cooperation between agents during the path selection process. The paths generated are proven to satisfy inter-agent constraints, such as collision avoidance, and a set of simulation and experimental results verify the algorithm's performance. A small scale rover is also presented as part of a practical test platform for the DMA-RRT algorithm.by Vishnu R. Desaraju.S.M

Advanced Sensing and Control for Connected and Automated Vehicles

Connected and automated vehicles (CAVs) are a transformative technology that is expected to change and improve the safety and efficiency of mobility. As the main functional components of CAVs, advanced sensing technologies and control algorithms, which gather environmental information, process data, and control vehicle motion, are of great importance. The development of novel sensing technologies for CAVs has become a hotspot in recent years. Thanks to improved sensing technologies, CAVs are able to interpret sensory information to further detect obstacles, localize their positions, navigate themselves, and interact with other surrounding vehicles in the dynamic environment. Furthermore, leveraging computer vision and other sensing methods, in-cabin humans’ body activities, facial emotions, and even mental states can also be recognized. Therefore, the aim of this Special Issue has been to gather contributions that illustrate the interest in the sensing and control of CAVs

Actuators for Intelligent Electric Vehicles

This book details the advanced actuators for IEVs and the control algorithm design. In the actuator design, the configuration four-wheel independent drive/steering electric vehicles is reviewed. An in-wheel two-speed AMT with selectable one-way clutch is designed for IEV. Considering uncertainties, the optimization design for the planetary gear train of IEV is conducted. An electric power steering system is designed for IEV. In addition, advanced control algorithms are proposed in favour of active safety improvement. A supervision mechanism is applied to the segment drift control of autonomous driving. Double super-resolution network is used to design the intelligent driving algorithm. Torque distribution control technology and four-wheel steering technology are utilized for path tracking and adaptive cruise control. To advance the control accuracy, advanced estimation algorithms are studied in this book. The tyre-road peak friction coefficient under full slip rate range is identified based on the normalized tyre model. The pressure of the electro-hydraulic brake system is estimated based on signal fusion. Besides, a multi-semantic driver behaviour recognition model of autonomous vehicles is designed using confidence fusion mechanism. Moreover, a mono-vision based lateral localization system of low-cost autonomous vehicles is proposed with deep learning curb detection. To sum up, the discussed advanced actuators, control and estimation algorithms are beneficial to the active safety improvement of IEVs

Advanced Mobile Robotics: Volume 3

Mobile robotics is a challenging field with great potential. It covers disciplines including electrical engineering, mechanical engineering, computer science, cognitive science, and social science. It is essential to the design of automated robots, in combination with artificial intelligence, vision, and sensor technologies. Mobile robots are widely used for surveillance, guidance, transportation and entertainment tasks, as well as medical applications. This Special Issue intends to concentrate on recent developments concerning mobile robots and the research surrounding them to enhance studies on the fundamental problems observed in the robots. Various multidisciplinary approaches and integrative contributions including navigation, learning and adaptation, networked system, biologically inspired robots and cognitive methods are welcome contributions to this Special Issue, both from a research and an application perspective

Optimization and Mathematical Modelling for Path Planning of Co-operative Intra-logistics Automated Vehicles

Small indoor Autonomous Vehicles have revolutionized the operation of pick-pack-and-ship warehouses. The challenges for path planning and co-operation in this domain stem from uncontrolled environments including workspaces shared with humans and human-operated vehicles. Solutions are needed which scale up to the largest existing sites with thousands of vehicles and beyond. These challenges might be familiar to anyone modelling road traffic control with the introduction of Autonomous Vehicles, but key differences in the level of decision autonomy lead to different approaches to conflict-resolution. This thesis proposes a decomposition of site-wide conflict-free motion planning into individual shortest paths though a roadmap representing the free space across the site, zone-based speed optimization to resolve conflicts in the vicinity of one intersection and individual path optimization for local obstacles. In numerical tests the individual path optimization based on clothoid basis functions created paths traversable by different vehicle configurations (steering rate limit, lateral acceleration limit and wheelbase) only by choosing an appropriate maximum longitudinal speed. Using two clothoid segments per convex region was sufficient to reach any goal, and the problem could be solved reliably and quickly with sequential quadratic programming due to the approximate graph method used to determine a good sequence of obstacle-free regions to the local goal. A design for zone-based intersection management, obtained by minimizing a linear objective subject to quadratic constraints was refined by the addition of a messaging interface compatible with the path adaptations based on clothoids. A new approximation of the differential constraints was evaluated in a multi-agent simulation of an elementary intersection layout. The proposed FIFO ordering heuristic converted the problem into a linear program. Interior point methods either found a solution quickly or showed that the problem was infeasible, unlike a quadratic constraint formulation with ordering flexibility. Subsequent tests on more complex multi-lane intersection geometries showed the quadratic constraint formulation converged to significantly better solutions than FIFO at the cost of longer and unpredictable search time. Both effects were magnified as the number of vehicles increased. To properly address site-wide conflict-free motion planning, it is essential that the local solutions are compatible with each other at the zone boundaries. The intersection management design was refined with new boundary constraints to ensure compatibility and smooth transitions without the need for a backup system. In numerical tests it was found that the additional boundary constraints were sufficient to ensure smooth transitions on an idealized map including two intersections

New Approaches in Automation and Robotics

The book New Approaches in Automation and Robotics offers in 22 chapters a collection of recent developments in automation, robotics as well as control theory. It is dedicated to researchers in science and industry, students, and practicing engineers, who wish to update and enhance their knowledge on modern methods and innovative applications. The authors and editor of this book wish to motivate people, especially under-graduate students, to get involved with the interesting field of robotics and mechatronics. We hope that the ideas and concepts presented in this book are useful for your own work and could contribute to problem solving in similar applications as well. It is clear, however, that the wide area of automation and robotics can only be highlighted at several spots but not completely covered by a single book

PLANNING UNDER UNCERTAINTIES FOR AUTONOMOUS DRIVING ON URBAN ROAD

Ph.DDOCTOR OF PHILOSOPH

Deep Reinforcement Learning for the Velocity Control of a Magnetic, Tethered Differential-Drive Robot

The ROBOPLANET Altiscan crawler is a magnetic-wheeled, differential-drive robot being explored as an option to aid, if not completely replace, humans in the inspection and maintenance of marine vessels. Velocity control of the crawler is a crucial part in establishing trust and reliability amongst its operators. However, thanks to the crawler's elongated, magnetic wheels and umbilical tether, it operates in a complex environment rich with nonlinear dynamics which makes control challenging. Model-based approaches for the control of a robot that aim to mathematically formalize the physics of the system require an in-depth knowledge of the domain. Reinforcement learning (RL) is a trial-and-error-based approach that can solve control problems in nonlinear systems. To accommodate for high-dimensionality and continuous state spaces, deep neural networks (DNNs) can be used as nonlinear function approximators to extend RL, creating a method known as deep reinforcement learning (DRL). DRL coupled with a simulated environment provides a way for a model to learn physics-naive control. The research conducted in this thesis explored the efficacy of a DRL algorithm, proximal policy optimization (PPO), to learn the velocity control of the Altiscan crawler by modeling its operating environment in a novel, GPU-accelerated simulation software called Isaac Gym. The approaches evaluated the error between measured base velocities of the crawler as a result of the actions provided by the DRL model and target velocities in six different environments. Two variants of PPO, standard and recurrent, were compared against the inverse velocity kinematics model of a differential-drive robot. The results show that velocity control in simulation is possible using PPO, but evaluation on the real crawler is needed to come to a meaningful conclusion.M.S

Engineering Dynamics and Life Sciences

From Preface: This is the fourteenth time when the conference “Dynamical Systems: Theory and Applications” gathers a numerous group of outstanding scientists and engineers, who deal with widely understood problems of theoretical and applied dynamics. Organization of the conference would not have been possible without a great effort of the staff of the Department of Automation, Biomechanics and Mechatronics. The patronage over the conference has been taken by the Committee of Mechanics of the Polish Academy of Sciences and Ministry of Science and Higher Education of Poland. It is a great pleasure that our invitation has been accepted by recording in the history of our conference number of people, including good colleagues and friends as well as a large group of researchers and scientists, who decided to participate in the conference for the first time. With proud and satisfaction we welcomed over 180 persons from 31 countries all over the world. They decided to share the results of their research and many years experiences in a discipline of dynamical systems by submitting many very interesting papers. This year, the DSTA Conference Proceedings were split into three volumes entitled “Dynamical Systems” with respective subtitles: Vibration, Control and Stability of Dynamical Systems; Mathematical and Numerical Aspects of Dynamical System Analysis and Engineering Dynamics and Life Sciences. Additionally, there will be also published two volumes of Springer Proceedings in Mathematics and Statistics entitled “Dynamical Systems in Theoretical Perspective” and “Dynamical Systems in Applications”

Energy Efficient Control of Hydrostatic Drive Transmissions: A Nonlinear Model-Based Approach

The high standard of living in industrial countries is based on the utilization of machines. In particular, the tasks performed with hydraulic work machines (HWMs) are essential in numerous industrial fields. Agriculture, mining, and construction are just a few examples of the lines of business that would be inconceivable today without HWMs. However, rising oil prices and competing technologies are challenging the manufacturers of these machines to improve their fuel economy.Despite the fact that energy efficiency research of hydraulic systems has been active for more than a decade, there seems to be a significant gap between industry and academia. The manufacturers of HWMs have not adopted, for example, novel system layouts, prototype components, or algorithms that require powerful control units in their products.The fuel economy of HWMs can be increased by utilizing system information in control algorithms. This cost-effective improvement enables operation in challenging regions and closer to the operating boundaries of the system. Consequently, the information about the system has to be accurate. For example, reducing the rotational speed of the engine has proven effective in improving the energy efficiency, but it increases the risk of even stalling the engine, for instance in situations where the power generation cannot meet the high transient demand. If this is considered in the controller with low uncertainty, fuel economy can be improved without decreasing the functionality of the machine.This thesis studies the advantages of model-based control in the improvement of the fuel economy of HWMs. The focus is on hydrostatic drive transmissions, which is the main consumer of energy in certain applications, such as wheel loaders.We started by developing an instantaneous optimization algorithm based on a quasi-static system model. The control commands of this fuel optimal controller (FOC) were determined based on cost function, which includes terms for fuel economy, steady-state velocity error, and changes in the control commands.Although the use of quasi-static models is adequate for steady-state situations, the velocity tracking during transients and under load changes has proven to be inadequate. To address this issue, a high-performance velocity-tracking controller was devised. Full state feedback was assumed, and we resorted to a so-called D-implementation, which eliminates, for example, the need for the equilibrium values of pressure signals. The nonlinearities of the system were considered with the state-dependent parameters of the linear model.In the next step, a nonlinear model predictive controller combined fuel economy control and velocity tracking. To the best of the author’s knowledge, this is the first time that the model predictive control scheme has been utilized with such a detailed system model that also considers the hydraulic efficiencies and torque generation of the engine. This enables utilizing the controller as a benchmark of control algorithms for non-hybrid hydrostatic drive transmissions that do not require information about the future.The initial tests of all the controllers were conducted with a validated simulation model of a research platform machine, a five-ton municipal tractor. In addition, the FOC and velocity-tracking controller were implemented into the control system of the machine. The practical worth of the FOC was proven with a relatively unique field experiment set-up that included, for example, an online measurement system of fuel consumption and autonomous path following. The fuel economy improved up to 16.6% when compared with an industrial baseline controller. The devised velocity-tracking concept was also proven as a significant reduction of error was observed in comparison with classic literature solutions, namely state feedback and proportional-integral-derivative controllers