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Passivity Based Model Predictive Control For Mobile Vehicle Motion Planning
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Book Synopsis Passivity-Based Model Predictive Control for Mobile Vehicle Motion Planning by : Adnan Tahirovic
Download or read book Passivity-Based Model Predictive Control for Mobile Vehicle Motion Planning written by Adnan Tahirovic and published by Springer Science & Business Media. This book was released on 2013-04-18 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: Passivity-based Model Predictive Control for Mobile Vehicle Navigation represents a complete theoretical approach to the adoption of passivity-based model predictive control (MPC) for autonomous vehicle navigation in both indoor and outdoor environments. The brief also introduces analysis of the worst-case scenario that might occur during the task execution. Some of the questions answered in the text include: • how to use an MPC optimization framework for the mobile vehicle navigation approach; • how to guarantee safe task completion even in complex environments including obstacle avoidance and sideslip and rollover avoidance; and • what to expect in the worst-case scenario in which the roughness of the terrain leads the algorithm to generate the longest possible path to the goal. The passivity-based MPC approach provides a framework in which a wide range of complex vehicles can be accommodated to obtain a safer and more realizable tool during the path-planning stage. During task execution, the optimization step is continuously repeated to take into account new local sensor measurements. These ongoing changes make the path generated rather robust in comparison with techniques that fix the entire path prior to task execution. In addition to researchers working in MPC, engineers interested in vehicle path planning for a number of purposes: rescued mission in hazardous environments; humanitarian demining; agriculture; and even planetary exploration, will find this SpringerBrief to be instructive and helpful.
Book Synopsis Motion Planning for Mobile Robot with Uncertainty by : Jiawei Tang
Download or read book Motion Planning for Mobile Robot with Uncertainty written by Jiawei Tang and published by . This book was released on 2021 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis On motion planning and control for truck and trailer systems by : Oskar Ljungqvist
Download or read book On motion planning and control for truck and trailer systems written by Oskar Ljungqvist and published by Linköping University Electronic Press. This book was released on 2019-01-22 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the last decades, improved sensor and hardware technologies as well as new methods and algorithms have made self-driving vehicles a realistic possibility in the near future. Thanks to this technology enhancement, many leading automotive and technology companies have turned their attention towards developing advanced driver assistance systems (ADAS) and self-driving vehicles. Autonomous vehicles are expected to have their first big impact in closed areas, such as mines, harbors and loading/offloading sites. In such areas, the legal requirements are less restrictive and the surrounding environment is more controlled and predictable compared to urban areas. Expected positive outcomes include increased productivity and safety, reduced emissions and the possibility to relieve the human from performing complex or dangerous tasks. Within these sites, different truck and trailer systems are used to transport materials. These systems are composed of several interconnected modules, and are thus large and highly unstable while reversing. This thesis addresses the problem of designing efficient motion planning and feedback control frameworks for such systems. First, a cascade controller for a reversing truck with a dolly-steered trailer is presented. The unstable modes of the system is stabilized around circular equilibrium configurations using a gain-scheduled linear quadratic (LQ) controller together with a higher-level pure pursuit controller to enable path following of piecewise linear reference paths. The cascade controller is then used within a rapidly-exploring random tree (RRT) framework and the complete motion planning and control framework is demonstrated on a small-scale test vehicle. Second, a path following controller for a reversing truck with a dolly-steered trailer is proposed for the case when the obtained motion plan is kinematically feasible. The control errors of the system are modeled in terms of their deviation from the nominal path and a stabilizing LQ controller with feedforward action is designed based on the linearization of the control error model. Stability of the closed-loop system is proven by combining global optimization, theory from linear differential inclusions and linear matrix inequality techniques. Third, a systematic framework is presented for analyzing stability of the closed-loop system consisting of a controlled vehicle and a feedback controller, executing a motion plan computed by a lattice planner. When this motion planner is considered, it is shown that the closed-loop system can be modeled as a nonlinear hybrid system. Based on this, a novel method is presented for analyzing the behavior of the tracking error, how to design the feedback controller and how to potentially impose constraints on the motion planner in order to guarantee that the tracking error is bounded and decays towards zero. Fourth, a complete motion planning and control solution for a truck with a dolly-steered trailer is presented. A lattice-based motion planner is proposed, where a novel parametrization of the vehicle’s state-space is proposed to improve online planning time. A time-symmetry result is established that enhance the numerical stability of the numerical optimal control solver used for generating the motion primitives. Moreover, a nonlinear observer for state estimation is developed which only utilizes information from sensors that are mounted on the truck, making the system independent of additional trailer sensors. The proposed framework is implemented on a full-scale truck with a dolly-steered trailer and results from a series of field experiments are presented.
Book Synopsis Motion planning and feedback control techniques with applications to long tractor-trailer vehicles by : Oskar Ljungqvist
Download or read book Motion planning and feedback control techniques with applications to long tractor-trailer vehicles written by Oskar Ljungqvist and published by Linköping University Electronic Press. This book was released on 2020-04-20 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the last decades, improved sensor and hardware technologies as well as new methods and algorithms have made self-driving vehicles a realistic possibility in the near future. At the same time, there has been a growing demand within the transportation sector to increase efficiency and to reduce the environmental impact related to transportation of people and goods. Therefore, many leading automotive and technology companies have turned their attention towards developing advanced driver assistance systems and self-driving vehicles. Autonomous vehicles are expected to have their first big impact in closed environments, such as mines, harbors, loading and offloading sites. In such areas, the legal requirements are less restrictive and the surrounding environment is more controlled and predictable compared to urban areas. Expected positive outcomes include increased productivity and safety, reduced emissions and the possibility to relieve the human from performing complex or dangerous tasks. Within these sites, tractor-trailer vehicles are frequently used for transportation. These vehicles are composed of several interconnected vehicle segments, and are therefore large, complex and unstable while reversing. This thesis addresses the problem of designing efficient motion planning and feedback control techniques for such systems. The contributions of this thesis are within the area of motion planning and feedback control for long tractor-trailer combinations operating at low-speeds in closed and unstructured environments. It includes development of motion planning and feedback control frameworks, structured design tools for guaranteeing closed-loop stability and experimental validation of the proposed solutions through simulations, lab and field experiments. Even though the primary application in this work is tractor-trailer vehicles, many of the proposed approaches can with some adjustments also be used for other systems, such as drones and ships. The developed sampling-based motion planning algorithms are based upon the probabilistic closed-loop rapidly exploring random tree (CL-RRT) algorithm and the deterministic lattice-based motion planning algorithm. It is also proposed to use numerical optimal control offline for precomputing libraries of optimized maneuvers as well as during online planning in the form of a warm-started optimization step. To follow the motion plan, several predictive path-following control approaches are proposed with different computational complexity and performance. Common for these approaches are that they use a path-following error model of the vehicle for future predictions and are tailored to operate in series with a motion planner that computes feasible paths. The design strategies for the path-following approaches include linear quadratic (LQ) control and several advanced model predictive control (MPC) techniques to account for physical and sensing limitations. To strengthen the practical value of the developed techniques, several of the proposed approaches have been implemented and successfully demonstrated in field experiments on a full-scale test platform. To estimate the vehicle states needed for control, a novel nonlinear observer is evaluated on the full-scale test vehicle. It is designed to only utilize information from sensors that are mounted on the tractor, making the system independent of any sensor mounted on the trailer. Under de senaste årtiondena har utvecklingen av sensor- och hårdvaruteknik gått i en snabb takt, samtidigt som nya metoder och algoritmer har introducerats. Samtidigt ställs det stora krav på transportsektorn att öka effektiviteten och minska miljöpåverkan vid transporter av både människor och varor. Som en följd av detta har många ledande fordonstillverkare och teknikföretag börjat satsat på att utveckla avancerade förarstödsystem och självkörande fordon. Även forskningen inom autonoma fordon har under de senaste årtiondena kraftig ökat då en rad tekniska problem återstår att lösas. Förarlösa fordon förväntas få sitt första stora genombrott i slutna miljöer, såsom gruvor, hamnar, lastnings- och lossningsplatser. I sådana områden är lagstiftningen mindre hård jämfört med stadsområden och omgivningen är mer kontrollerad och förutsägbar. Några av de förväntade positiva effekterna är ökad produktivitet och säkerhet, minskade utsläpp och möjligheten att avlasta människor från att utföra svåra eller farliga uppgifter. Inom dessa platser används ofta lastbilar med olika släpvagnskombinationer för att transportera material. En sådan fordonskombination är uppbyggd av flera ihopkopplade moduler och är således utmanande att backa då systemet är instabilt. Detta gör det svårt att utforma ramverk för att styra sådana system vid exempelvis autonom backning. Självkörande fordon är mycket komplexa system som består av en rad olika komponenter vilka är designade för att lösa separata delproblem. Två viktiga komponenter i ett självkörande fordon är dels rörelseplaneraren som har i uppgift att planera hur fordonet ska röra sig för att på ett säkert sätt nå ett överordnat mål, och dels den banföljande regulatorn vars uppgift är att se till att den planerade manövern faktiskt utförs i praktiken trots störningar och modellfel. I denna avhandling presenteras flera olika algoritmer för att planera och utföra komplexa manövrar för lastbilar med olika typer av släpvagnskombinationer. De presenterade algoritmerna är avsedda att användas som avancerade förarstödsystem eller som komponenter i ett helt autonomt system. Även om den primära applikationen i denna avhandling är lastbilar med släp, kan många av de förslagna algoritmerna även användas för en rad andra system, så som drönare och båtar. Experimentell validering är viktigt för att motivera att en föreslagen algoritm är användbar i praktiken. I denna avhandling har flera av de föreslagna planerings- och reglerstrategierna implementerats på en småskalig testplattform och utvärderats i en kontrollerad labbmiljö. Utöver detta har även flera av de föreslagna ramverken implementerats och utvärderats i fältexperiment på en fullskalig test-plattform som har utvecklats i samarbete med Scania CV. Här utvärderas även en ny metod för att skatta släpvagnens beteende genom att endast utnyttja information från sensorer monterade på lastbilen, vilket gör det föreslagna ramverket oberoende av sensorer monterade på släpvagnen.
Book Synopsis Motion Planning for Autonomous Vehicles in Partially Observable Environments by : Taş, Ömer Şahin
Download or read book Motion Planning for Autonomous Vehicles in Partially Observable Environments written by Taş, Ömer Şahin and published by KIT Scientific Publishing. This book was released on 2023-10-23 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work develops a motion planner that compensates the deficiencies from perception modules by exploiting the reaction capabilities of a vehicle. The work analyzes present uncertainties and defines driving objectives together with constraints that ensure safety. The resulting problem is solved in real-time, in two distinct ways: first, with nonlinear optimization, and secondly, by framing it as a partially observable Markov decision process and approximating the solution with sampling.
Book Synopsis Safe Robot Navigation Among Moving and Steady Obstacles by : Andrey V. Savkin
Download or read book Safe Robot Navigation Among Moving and Steady Obstacles written by Andrey V. Savkin and published by Butterworth-Heinemann. This book was released on 2015-09-25 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: Safe Robot Navigation Among Moving and Steady Obstacles is the first book to focus on reactive navigation algorithms in unknown dynamic environments with moving and steady obstacles. The first three chapters provide introduction and background on sliding mode control theory, sensor models, and vehicle kinematics. Chapter 4 deals with the problem of optimal navigation in the presence of obstacles. Chapter 5 discusses the problem of reactively navigating. In Chapter 6, border patrolling algorithms are applied to a more general problem of reactively navigating. A method for guidance of a Dubins-like mobile robot is presented in Chapter 7. Chapter 8 introduces and studies a simple biologically-inspired strategy for navigation a Dubins-car. Chapter 9 deals with a hard scenario where the environment of operation is cluttered with obstacles that may undergo arbitrary motions, including rotations and deformations. Chapter 10 presents a novel reactive algorithm for collision free navigation of a nonholonomic robot in unknown complex dynamic environments with moving obstacles. Chapter 11 introduces and examines a novel purely reactive algorithm to navigate a planar mobile robot in densely cluttered environments with unpredictably moving and deforming obstacles. Chapter 12 considers a multiple robot scenario. For the Control and Automation Engineer, this book offers accessible and precise development of important mathematical models and results. All the presented results have mathematically rigorous proofs. On the other hand, the Engineer in Industry can benefit by the experiments with real robots such as Pioneer robots, autonomous wheelchairs and autonomous mobile hospital. First book on collision free reactive robot navigation in unknown dynamic environments Bridges the gap between mathematical model and practical algorithms Presents implementable and computationally efficient algorithms of robot navigation Includes mathematically rigorous proofs of their convergence A detailed review of existing reactive navigation algorithm for obstacle avoidance Describes fundamentals of sliding mode control
Book Synopsis DEVELOPMENT OF AUTONOMOUS VEHICLE MOTION PLANNING AND CONTROL ALGORITHM WITH D* PLANNER AND MODEL PREDICTIVE CONTROL IN A DYNAMIC ENVIRONMENT by :
Download or read book DEVELOPMENT OF AUTONOMOUS VEHICLE MOTION PLANNING AND CONTROL ALGORITHM WITH D* PLANNER AND MODEL PREDICTIVE CONTROL IN A DYNAMIC ENVIRONMENT written by and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract : The research in this report incorporates the improvement in the autonomous driving capability of self-driving cars in a dynamic environment. Global and local path planning are implemented using the D* path planning algorithm with a combined Cubic B-Spline trajectory generator, which generates an optimal obstacle free trajectory for the vehicle to follow and avoid collision. Model Predictive Control (MPC) is used for the longitudinal and the lateral control of the vehicle. The presented motion planning and control algorithm is tested using Model-In-the-Loop (MIL) method with the help of MATLAB® Driving Scenario Designer and Unreal Engine® Simulator by Epic Games®. Different traffic scenarios are built, and a camera sensor is configured to simulate the sensory data and feed it to the controller for further processing and vehicle motion planning. Simulation results of vehicle motion control with global and local path planning for dynamic obstacle avoidance are presented. The simulation results show that an autonomous vehicle follows a commanded velocity when the relative distance between the ego vehicle and an obstacle is greater than a calculated safe distance. When the relative distance is close to the safe distance, the ego vehicle maintains the headway. When an obstacle is detected by the ego vehicle and the ego vehicle wants to pass the obstacle, the ego vehicle performs obstacle avoidance maneuver by tracking desired lateral positions.
Book Synopsis Motion Planning for Mobile Robots Via Sampling-Based Model Predictive Optimization by : Damion D. Dunlap
Download or read book Motion Planning for Mobile Robots Via Sampling-Based Model Predictive Optimization written by Damion D. Dunlap and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Motion Planning for Mobile Robots Via Sampling-Based Model Predictive Optimization.
Book Synopsis Applications of Model Predictive Control to Vehicle Dynamics for Active Safety and Stability by : Craig Earl Beal
Download or read book Applications of Model Predictive Control to Vehicle Dynamics for Active Safety and Stability written by Craig Earl Beal and published by Stanford University. This book was released on 2011 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: Each year in the United States, thousands of lives are lost as a result of loss of control crashes. Production driver assistance systems such as electronic stability control (ESC) have been shown to be highly effective in preventing many of these automotive crashes, yet these systems rely on a sensor suite that yields limited information about the road conditions and vehicle motion. Furthermore, ESC systems rely on gains and thresholds that are tuned to yield good performance without feeling overly restrictive to the driver. This dissertation presents an alternative approach to providing stabilization assistance to the driver which leverages additional information about the vehicle and road that may be obtained with advanced estimation techniques. This new approach is based on well-known and robust vehicle models and utilizes phase plane analysis techniques to describe the limits of stable vehicle handling, alleviating the need for hand tuning of gains and thresholds. The resulting state space within the computed handling boundaries is referred to as a safe handling envelope. In addition to the boundaries being straightforward to calculate, this approach has the benefit of offering a way for the designer of the system to directly adjust the controller to accomodate the preferences of different drivers. A model predictive control structure capable of keeping the vehicle within the safe handling boundaries is the final component of the envelope control system. This dissertation presents the design of a controller that is capable of smoothly and progressively augmenting the driver steering input to enforce the boundaries of the envelope. The model predictive control formulation provides a method for making trade-offs between enforcing the boundaries of the envelope, minimizing disruptive interventions, and tracking the driver's intended trajectory. Experiments with a steer-by-wire test vehicle demonstrate that the model predictive envelope control system is capable of operating in conjunction with a human driver to prevent loss of control of the vehicle while yielding a predictable vehicle trajectory. These experiments considered both the ideal case of state information from a GPS/INS system and an a priori friction estimate as well as a real-world implementation estimating the vehicle states and friction coefficient from steering effort and inertial sensors. Results from the experiments demonstrated a controller that is tolerant of vehicle and tire parameterization errors and works well over a wide range of conditions. When real time sensing of the states and friction properties is enabled, the results show that coupling of the controller and estimator is possible and the model predictive control structure provides a mechanism for minimizing undesirable coupled dynamics through tuning of intuitive controller parameters. The model predictive control structure presented in this dissertation may also be considered as a general framework for vehicle control in conjunction with a human driver. The structure utilized for envelope control may also be used to restrict other vehicle states for safety and stability. Results are presented in this dissertation to show that a model predictive controller can coordinate a secondary actuator to alter the planar states and reduce the energy transferred into the roll modes of the vehicle. The systematic approach to vehicle stabilization presented in this dissertation has the potential to improve the design methodology for future systems and form the basis for the inclusion of more advanced functions as sensing and computing capabilities improve. The envelope control system presented here offers the opportunity to advance the state of the art in stabilization assistance and provides a way to help drivers of all skill levels maintain control of their vehicle.
Book Synopsis Interaction and Uncertainty-Aware Motion Planning for Autonomous Vehicles Using Model Predictive Control by : Jian Zhou
Download or read book Interaction and Uncertainty-Aware Motion Planning for Autonomous Vehicles Using Model Predictive Control written by Jian Zhou and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis MPC-BASED AUTONOMOUS DRIVING CONTROL WITH LOCALIZED PATH PLANNING FOR OBSTACLE AVOIDANCE AND NAVIGATING SIGNALIZED INTERSECTIONS by :
Download or read book MPC-BASED AUTONOMOUS DRIVING CONTROL WITH LOCALIZED PATH PLANNING FOR OBSTACLE AVOIDANCE AND NAVIGATING SIGNALIZED INTERSECTIONS written by and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract : Connected and autonomous vehicles are becoming the major focus of research for the industry and academia in the automotive field. Many companies and research groups have demonstrated the advantages and the requirement of such technology to improve the energy efficiency of vehicles, decrease the number of crash and road accidents, and control emissions. This research delves into improving the autonomy of self-driving vehicles by implementing localized path planning algorithms to introduce motion control for obstacle avoidance during uncertainties. Lateral path planning is implemented using the A* algorithm combined with piecewise Bezier curve generation which provides an optimum trajectory reference to avoid a collision. Model Predictive Control (MPC) is used to implement longitudinal and lateral control of the vehicle. The data from vehicle-to-everything (V2X) communication infrastructure is used to navigate through multiple signalized intersections. Furthermore, a new method of developing Advanced Driver Assistance Systems (ADAS) algorithms and vehicle controllers using Model-In-the-Loop (MIL) testing is explored with the use of PreScan®. With PreScan®, various traffic scenarios are modeled and the sensor data are simulated by using physics-based sensor models, which are fed to the controller for data processing and motion planning. Obstacle detection and collision avoidance are demonstrated using the presented MPC controller. The results of the proposed controller and the scope of the future work conclude the research.
Book Synopsis Adaptive Model-predictive Motion Planning for Navigation in Complex Environments by : Thomas M. Howard
Download or read book Adaptive Model-predictive Motion Planning for Navigation in Complex Environments written by Thomas M. Howard and published by . This book was released on 2009 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "Outdoor mobile robot motion planning and navigation is a challenging problem in artificial intelligence. The search space density and dimensionality, system dynamics and environmental interaction complexity, and the perceptual horizon limitation all contribute to the difficultly [sic] of this problem. It is hard to generate a motion plan between arbitrary boundary states that considers sophisticated vehicle dynamics and all feasible actions for nontrivial mobile robot systems. Accomplishing these goals in real time is even more challenging because of dynamic environments and updating perception information. This thesis develops effective search spaces for mobile robot trajectory generation, motion planning, and navigation in complex environments. Complex environments are defined as worlds where locally optimal motion plans are numerous and where the sensitivity of the cost function is highly dependent on state and motion model fidelity. Examples include domains where obstacles are prevalent, terrain shape is varied, and the consideration of terramechanical effects is important. Three specific contributions are accomplished. First, a model-predictive trajectory generation technique is developed that numerically linearizes and inverts general predictive motion models to determine parameterized actions that satisfy the two-point boundary value problem. Applications on a number of mobile robot platforms (including skidsteered field robots, planetary rovers with actively articulating chassis, mobile manipulators, and autonomous automobiles) demonstrate the versatility and generality of the presented approach. Second, an adaptive search space is presented that exploits environmental information to maintain feasibility and locally optimize the mapping between nodes and states. Sequential search in the relaxed motion planning graph is shown to produce better (shorter/faster/lower-risk) trajectories in dense obstacle fields without modifying the graph topology. Results demonstrate that a coarse, adaptive search space can produce better solutions faster than dense, fixed search spaces in sufficiently complex environments. Lastly, a receding-horizon model-predictive control method that exploits structure from sequential search to determine trajectory following actions is presented. The action space is parameterized by the regional motion plan and subsequently relaxed through unconstrained optimization. Examples are shown to effectively navigate intricate paths in a natural environment while maintaining a constant horizon."
Book Synopsis Application of Model-based Control in Autonomous Vehicle Chassis Control and Motion Planning by :
Download or read book Application of Model-based Control in Autonomous Vehicle Chassis Control and Motion Planning written by and published by . This book was released on 2021 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automatic control systems are found ubiquitously in today's automobiles. This dissertation focuses on the development of control systems for autonomous vehicle applications using model-based control design approaches. We start with the control system development for chassis actuators. Firstly, we develop a closed loop torque overlay control system to improve the steering feel of an electric power-assisted steering (EPAS) system. This system has a reference model, a rack force estimator, and a tracking controller. A target steering feel is generated from a reference model with an rack force estimate to reflect the actual vehicle operating conditions. The performance of the proposed control system is evaluated through simulation and a hardware-in-the-loop test. Secondly, we present an EMB clamping force control system is developed that addresses several major challenges in practical implementation. A nonlinear EMB model including a novel clamping force model is introduced. A clamping force estimation and contact detection algorithm is proposed that requires only the existing measurements. Furthermore, a unified architecture is proposed to realize a smooth transition between gap closing and clamping force tracking. The performance of the control system is evaluated based on simulation. In the second part, we developed a hierarchical nonlinear model predictive control (NMPC) framework for autonomous vehicle motion planning and control. At low level, a trajectory tracking NMPC is used to track the reference trajectory given by the highlevel motion planner. A frequency shaped objective function is used to incorporate lower level actuator dynamics. Real-time implementation is realized by a fast NMPC algorithm based on RTI with condensing and control parameterization. Simulation results show that the control system yields good performance under various road conditions when the vehicle is operating at its handling limit. A high-level motion planner is formulated in the NMPC framework incorporating high order control barrier function (HOCBF) based collision avoidance constraint. The motion planner is able to generate dynamically feasible trajectories with respect to various constraints. Simulations of the motion planner and trajectory tracking controller demonstrate the effectiveness of the proposed control framework.
Book Synopsis Omnidirectional Tilt-Rotor Flying Robots for Aerial Physical Interaction by : Karen Bodie
Download or read book Omnidirectional Tilt-Rotor Flying Robots for Aerial Physical Interaction written by Karen Bodie and published by Springer Nature. This book was released on 2024-01-21 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book deals with the study of tilt-rotor omnidirectional aerial robots and their application to aerial physical interaction tasks. Omnidirectional aerial robots possess decoupled translational and rotational dynamics, which are important for stable and sustained aerial interaction. The additional ability to dynamically re-orient thrust vectors opens the door to a wide array of possible morphologies and system capabilities. Through modeling, control, prototype design, and experimental evaluation, this book presents a comprehensive methodology and examples for the development of a novel tilt-rotor aerial manipulator. This work serves as a guide for envisioning and constructing innovative systems that will advance the frontier of aerial manipulation.
Book Synopsis Eingeschränkte Model Predictive Control für Echtzeit-Teleoperation Bewegungsplanung by : Mingming Wang
Download or read book Eingeschränkte Model Predictive Control für Echtzeit-Teleoperation Bewegungsplanung written by Mingming Wang and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Model Predictive Control Schemes For Autonomous Ground Vehicle by : Solomon S. Oyelere
Download or read book Model Predictive Control Schemes For Autonomous Ground Vehicle written by Solomon S. Oyelere and published by LAP Lambert Academic Publishing. This book was released on 2013 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: The application of Model Predictive Control (MPC) to fast systems like autonomous ground vehicles (AGV) or mobile robots is thoroughly investigated in this book. The control of Autonomous ground vehicles (AGV) is challenging because of nonholonomic constraints, uncertainties, speed and accuracy of controls and the vehicle's terrain of operation. This work develops a Model Predictive Control (MPC) algorithm for the autonomous ground vehicle (AGV).
Book Synopsis Robust MPC Based Motion Planning and Control of Autonomous Ground Vehicles by : Vivek Bithar
Download or read book Robust MPC Based Motion Planning and Control of Autonomous Ground Vehicles written by Vivek Bithar and published by . This book was released on 2020 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: The motion planning layer of an autonomous software stack is responsible for the planning of an obstacle avoidance path in all possible scenarios. Emergency scenarios where maneuvers that must be planned are at the limits of vehicle handling are the most challenging path planning problems due to the presence of inherent uncertainties in the modeling, localization/state estimation, and the environment perception.
