Top PDF Guidance and Path-Planning Systems for Autonomous Vehicles

Guidance and Path-Planning Systems for Autonomous Vehicles

Guidance and Path-Planning Systems for Autonomous Vehicles

the time-varying ∆ equation from a stability viewpoint is investigated. The sec- ond (and minor) contribution is the consideration of a 5-DOF vehicle kinematic model (common for torpedo-shaped underwater vehicles which do not control the roll angle) and the influence of the coupling between the horizontal and vertical planes on the expression for the sideslip angle. This led to the third contribution, which is a transformation of the LOS guidance in quaternion form for both the uncoupled and the coupled cases. The transformation is based on exploiting very simple trigonometric properties and the geometry of the LOS guidance. The fourth contribution is an integral LOS guidance law capable of eliminating the errors in- duced by constant external disturbances. The method is formulated using absolute velocity-based vehicle kinematics and simple Lyapunov-based analysis. The fifth contribution moves a few steps further and presents two adaptive integral LOS guidance laws which compensate for the errors induce by ocean currents. These methods are based on the vehicle kinematics in relative-velocity form. This is a very useful result for underwater vehicles, where absolute velocity measurements might not be available. The effect of the current on the direction normal to the direction of motion (that is, the force inducing the cross-track error) is estimated, and stability results for curved paths are also given. The sixth contribution is the development of a guidance technique where, in addition to the LOS guidance for minimizing the cross-track error, surge velocity commands are generated as well in order to minimize the along-track error, hence satisfying constraints related to the path-tracking (or trajectory-tracking) motion control scenario. Finally, the path-tracking solution is combined with the indirect adaptive integral LOS so as to achieve path tracking under the influence of ocean currents, which also results in es- timating all the parameters of the current (that is, current velocity and orientation w.r.t. the inertial frame).
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The Policy Trajectories of Autonomous Vehicles

The Policy Trajectories of Autonomous Vehicles

In September 2016, the U.S. Department of Transportation through its National Highway Traffic Safety Administration (NHTSA) published non-binding performance guidance in order to facilitate the development of AVs, offering a consistent regulatory regime to the carmakers and technology companies competing to bring self-driving cars to market. 3 This document included a 15-point safety assessment for developers and model state policy. One year later, NHTSA updated its guidance, 4 and in the same week, the U.S. House passed H.R. 3388—the SELF-DRIVE Act—a standard set- ting piece of legislation that seeks to further advance the innovation of this technology. 5 Both the NHTSA guid- ance and H.R. 3388 have important implications, as we will discuss, but what these measures make immedi- ately clear is that federal lawmakers and regulators are working alongside private industry to boost innovation in ways that, in other policy contexts, they so often do not.
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Smooth Path Generation Based on B´ezier Curves for Autonomous Vehicles

Smooth Path Generation Based on B´ezier Curves for Autonomous Vehicles

Choi has presented two path planning algorithms based on B´ezier curves for autonomous vehicles with waypoints and corridor constraints [2]. Both algorithms join cubic B´ezier curve segments smoothly to generate the reference trajectory for vehicles to satisfy the path constraints. Also, both algorithms are constrained in that the path must cross over a bisector line of corner area such that the tangent at the crossing point is normal to the bisector. Addition- ally, that paper discuss the constrained optimization prob- lem that optimizes the resulting path for user-defined cost function. Since the B´ezier curve is uniquely defined by its control points, the optimization problem is parameterized by the location of control points. Even though the simu- lation provided in that paper has shown the generation of
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Autonomous Parallel Parking of a Car-Like Mobile Robot with Geometric Path Planning

Autonomous Parallel Parking of a Car-Like Mobile Robot with Geometric Path Planning

With the advancement of technology making everything so convenient in these days and ages, autonomous system is very interesting area in the innovating technology. One of the advanced booming technologies for the improvement of human race is autonomous taxi mobile transportation system. It is working well in a district area but it still thrives on making more comfortable. This research will be one point of supporting roles for automobile in parallel parking. An autonomous parallel parking of a car-like mobile robot has been developed in this study. The ultrasonic range sensors are used to detect the working environments and design an s-shaped trajectory between two parallel parked vehicles. One trail maneuver system is used to be moving along the s- shaped trajectory parking path. The s-shaped trajectory is purely based on the geometric approach path planning method. The proposed method is not dependent on the initial pose of the robot but it must be parallel with the parking space. Sensor data are used as the main decision part to change the parking states instance of to find the parking space and adjust robot orientation. Fuzzy filter is applied to stabilize the sensor data and give a quick response input. The working environment is constrained by the wall and sensor arrangement. Visual studio 2013 is used for the user interface window. The effectiveness of the proposed method is demonstrated through some experimental results with a car-like mobile robot.
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Goal Directed Approach to Autonomous Motion Planning for Unmanned Vehicles

Goal Directed Approach to Autonomous Motion Planning for Unmanned Vehicles

3. GOAL DIRECTED RRT PATH PLANNER A different variations 10 has been proposed in this paper to the basic RRT algorithm. The current existing variations to the algorithm are classified under three categories as methods for generating random state, methods to extend towards a new node and methods to add bias to the goal. Every type of variation has its own advantages. The goal directed RRT path planner introduces a new method of adding bias towards the goal primarily by using a NEAREST_TO_GOAL function resulting in faster rate of convergence of the planner. And for certain reasons the experiments also show a reduction in solution length.
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Research Article Path Planning for Autonomous Vehicle Based on a Two-Layered Planning Model in Complex Environment

Research Article Path Planning for Autonomous Vehicle Based on a Two-Layered Planning Model in Complex Environment

In recent years, in order to avoid multiple dynamic obstacles, FSVM has been used by Suresh et al. [12] to secure a collision-free path. The results draw a conclusion that this method is effective. This method is proved in simulations by constructing the fuzzy rules using simple evaluation data. A real-time path planning algorithm has been put into ap- plication by Chu et al. [13]. After generating some paths on the basis of predefined waypoints, the optimal function deals with the selection of final safe and smooth path. Makarem and Gillet [14] have developed a method that contains a navigation function suitable for autonomous vehicle but does not consider the influence of obstacles. “Tentacle method” [15] is another way that has been proposed by Chebly et al. By considering the vehicle as the origin, the method develops a set of virtual tentacles that indicate possible paths for vehicle. Then, the best path is chosen according to the evaluation function. In complex dynamic environment, Moreau et al. [16] have investigated an im- proved curve planning method. All sensors required for obstacle detection are considered in an autonomous vehicle. Taking equality constraints into account, the planning problem is transferred to an optimal problem. Then, La- grangian and gradient-based methods are used to cover it. In [17], Tazir et al. make a real-time planning based on the combination of two methods. The robot uses genetic al- gorithms and Dijkstra algorithm to avoid static obstacles. The wait/accelerate concept leads to travel in local dynamic environment. This method is test-efficient but lacks con- sideration of robot kinematic constraints. In order to let autonomous vehicles drive along a basic path and at the same time avoid obstacles and meet the vehicle kinematic constraints, researchers have proposed an original inte- grated local trajectory planning and tracking control (ILTPTC) framework [18]. In this framework, MPC based planning method is used, which could satisfy the require- ment of vehicle kinematic but cannot meet the need of real time.
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Comparison of Path Tracking and Torque-Vectoring Controllers for Autonomous Electric Vehicles

Comparison of Path Tracking and Torque-Vectoring Controllers for Autonomous Electric Vehicles

Roborace, the world’s first driverless electric racing series. He received a degree in mechanical engineering and vehicle design from the University of Hertfordshire, Hatfield, U.K., in 1996. His experience spans 16 years working for Bernie Ecclestone’s Formula One Management leading all significant technical projects from the construction of new circuits to fully automated cars and camera tracking systems, and the implementation of the Formula One Global Media Network. He left to join the executive team of a media start up pioneering multiple cutting edge technologies for TV studios and has also consulted on technology strategy for organisations including the BBC and McCann Worldgroup.
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Optimal algorithm design for transfer path planning for unmanned aerial vehicles

Optimal algorithm design for transfer path planning for unmanned aerial vehicles

The majority of operational UAV control is still carried out by a remote operator phys- ically controlling the UAV from a base station. The next generation of UAVs however aim to take this to the next level. By providing a UAV with the ability to sense its surroundings and analyse the information it is possible for the UAV to generate its own control decisions based on mission parameters and inherent design limitations. If you consider the situation that automation is trying to replicate; a pilot flying an aircraft; the steps to full automation appear more complex than at first glance. Consider an obstacle avoidance task: A pilot is capable of seeing an obstacle and carrying out an avoidance manoeuvre and then continuing with the mission at hand. For an autonomous UAV (AUAV) this task is more complex: the AUAV first has to have some method through which to view its surroundings (usually some form of camera is used), it then has to take this information and identify the potential obstacles, next it must select an appropriate control action to avoid the obstacle and finally it has to recover from this avoidance manoeuvre back to its normal operating mode. A similar situation occurs for a UAV simply trying to travel between two points. The UAV is likely to be programmed with its desired destination but then is required to work out a way to travel from its current position to its destination and arrive in the correct configuration to carry out its next mission phase. It is also imperative that the flight limitations of the UAV are not broken during the transfer as this could result in the loss of the vehicle. For a human operator these tasks are relatively simple whereas it is difficult for a UAV.
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Strategic planning for autonomous systems over long horizons

Strategic planning for autonomous systems over long horizons

Planning plays a role in achieving long-term behaviour (per- sistent autonomy) without human intervention. Such be- haviour engenders plans which are expected to last over many hours, or even days. Such a problem is too large for cur- rent planners to solve as a single planning problem, but is well-suited to decomposition and abstraction planning tech- niques. We present a novel approach to bottom-up decompo- sition into a two-layer hierarchical structure, which dynami- cally constructs planning problems at the abstract layer of the hierarchy using solution plans from the lower layer. We evaluate this approach in the context of persistent au- tonomy in autonomous underwater vehicles, showing that compared to strictly top-down approaches the bottom-up ap- proach leads to more robust solution plans of higher quality.
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On Path Planning Strategies for Networked Unmanned Aerial Vehicles

On Path Planning Strategies for Networked Unmanned Aerial Vehicles

There are several planning strategies proposed for ground robots [1], delivery systems [5], autonomous high-speed, fixed- wing UAV networks [6], or mobile sensor networks [7] with different objectives and constraints. Applications range from snow removal, lawn mowing, floor cleaning, to surveillance, mobile target tracking, chemical or hazardous material detec- tion and containment, or to any combination of localization and navigation problems (see [8], [9], [10]). While some algorithms use prior information and have exact or partial decomposition of the areas, others use sensor-based informa- tion in unknown environments to make navigation decisions. Algorithms exist that try to minimize the path traveled or time or energy required to achieve a goal. These different schemes have some common building blocks, such as static or dynamic area decomposition, cooperative or non-cooperative actions, individual or collective decisions, static or adaptive behavior. Therefore, in this paper, we study two different approaches that consider some intuitive combination of these building blocks. The remainder of the paper is organized as follows. The system model and metrics of interest are given in Section II. The methodologies are introduced in Section III. Results are given in Section IV and the paper is concluded in Section V.
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Path planning for unmanned aerial vehicles using visibility line based methods

Path planning for unmanned aerial vehicles using visibility line based methods

Many researchers have concentrated on reducing the computational effort required to create the VL network as it is computationally expensive in obstacle-rich environments. Reduced computation time is useful for real-time application. Wooden & Egerstedt [72] derived a significantly reduced roadmap for unstructured polygonal environments suitable for real-time path planning application of outdoor robots. The method called Oriented Visibility Graph (OVG), attached an onboard stereo-based sensor to the robot to detect the obstacles and created the polygonised maps to support the use of the planner. In order to improve the performance over runs, the graphs were saved between runs and dynamic update rules were carried out. Also, the algorithm that was proposed Tokuta [10], as explained above, is suitable to be applied in real time path planning as the VPIA runs in parallel which reduces the computation time. Another real time path planning research project based on VL was done by Huang and Chung [74]. They proposed a method called Dynamic Visibility Graph (DVG), which was claimed to be fast for constructing a reduced roadmap through polygonal obstacles within an active region. DVG enormously decreases the computation time for reconstructing the map and hence is suitable for real time path-planning for single and multiple autonomous vehicles. However, it is difficult to define the area of active region. Other methods for reducing the complexity of VL were proposed by [75, 111]. Both methods were claimed to have low computation loads. Omar and Gu [112] proposed a path planning method, which is based on VL, called Base Line Oriented Visibility Line (BLOVL) to find paths in short time by reducing the numbers of obstacles during the paths calculation. BLOVL was proven through simulations to have paths that are identical to those of conventional VL most of the times.
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Evaluating Driving Performance of a Novel Behavior Planning Model on Connected Autonomous Vehicles

Evaluating Driving Performance of a Novel Behavior Planning Model on Connected Autonomous Vehicles

This three-layer division of control is a standard framework used for au- tonomous driving and many other applications in robotics [6]. Good vehicle control approaches have been known for a while (Model-Predictive Control, Control Lyapunov, etc.) [7][8], and path planning is often done by simply try- ing to track the center line of a lane. However, behavior planning currently has great potential for improvement, especially in situations with multiple con- nected vehicles. Exchanging information with other neighbors can allow a ve- hicle’s knowledge about the road to extend further than its own field of vision. This work will focus on the implementation of a proposed behavior planning algorithm for connected autonomous vehicles.
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The role of adaptive mission planning and control in persistent autonomous underwater vehicles presence

The role of adaptive mission planning and control in persistent autonomous underwater vehicles presence

The lack of sufficient trials is by far the major factor stopping the adoption of adaptive mission planning software. Increasing the number of trial deployments is an obvious recommendation to mitigate this. Making the results of the trial deployments public and widely accessible is of equally great importance. One key point here is what makes a trial a successful test? Is it the identification of a given target or feature? is it the time taken to find the target? the number of attempts? These criteria need to be defined before the AUV community starts discussing validation of the technology. Once criteria have been defined then it becomes a question of identifying the number of successful tests to statistically quantify the confidence that given criteria is met. To address this, one or more groups with well established capability for adaptive mission planning could offer to run missions with objectives and criteria set by groups not having that capability, as wider demonstrators. Such a set of actions will increase the community confidence in the precision of adaptive mission planning systems.
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COLREGS compliant path planning for riverine autonomous surface VESSLE

COLREGS compliant path planning for riverine autonomous surface VESSLE

Navigation of ships mainly depends on the mariners’ experience and judgment. However, a lot of the accidents at sea are attributed to human error. Thus the International Marine Organisation (IMO) defined The International Regulations for Preventing Collisions at Sea (COLREGs) [1]. COLREGs are marine traffic rules for navigators to make evasive manoeuvres to avoid collision when encounters other ships in open sea and confined water area. Since navigation of ASV is expected to imitate mariners’ behaviors, COLREGs are supposed to be included in the guidance system for case that the ASV encounters other ships, and so as to other static and dynamic objects.
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Localization for Autonomous Vehicles

Localization for Autonomous Vehicles

Then what is needed to solve effective localization for autonomous ve- hicles? Firstly, we need to understand what is needed in terms of accuracy, availability and price. These requirements are far from obvious, and could warrant a thesis in and by itself, but a short description of how one can reason about it is included in Chapter 2. Secondly, we need a map that connects the observable landmarks with the possibly unobservable, static features of the road that are needed for path planning, i.e. drivable surface, static obstacles, etc. A few thoughts on this map have also been included in Chapter 2. Thirdly, we need sensors which are capable of observing the landmarks, and sensor models which describe how the observations relate to the physical world. In Chapter 3, radars are briefly described, while cameras and global navigation satellite systems such as GPS, are described in further detail, with a focus on how they can be used for localization purposes. Lastly, we need algorithms that combine the given maps and the measurements into estimates of position and orientation. This is achieved using the Bayesian estimation framework described in Chapter 4, where we see how to combine the models of various sensors with a motion model for the vehicle to arrive at probabilistic models of position and orientation.
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Velocity field path-planning for single and multiple unmanned ariel vehicles

Velocity field path-planning for single and multiple unmanned ariel vehicles

A velocity field approach to UAV path-planning has been presented which allows single or multiple UAVs to manoeuvre between a start point S and goal point G without collision with static obstacles or other UAVs in a formation. Unlike heuristic path-planning algo- rithms, the velocity field algorithm presented here is based on math- ematical rigour in that the properties of the Laplace equation can be invoked to demonstrate that the field will generate a unique path which is guaranteed to reach the goal G. In addition, since the velocity field can be generated analytically, the computational over- head to implement the algorithm is minimal, allowing autonomous operations with on-board path-planning.
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Simulation for Improvement of Dynamic Path Planning in Autonomous Search and Rescue Robots

Simulation for Improvement of Dynamic Path Planning in Autonomous Search and Rescue Robots

While this project does not aim to solve the SLAM problem, the style of map building is similar to other autonomous systems such as the type created for this project and highlights the kind of issues which may be faced when mapping a terrain using relative measurements. Maps are built which revolve around the geometrical relevance/consistency (shapes, size, spacing etc.) of the terrain and being as accurate as possible in this respect, as opposed to identifying topologi- cal features such as altitudes/contour lines which are more relevant to large scale approximate mapping. Over time, as features are observed from various posi- tions, the accuracy of estimates/modelling increases. Because all measurements within the model are relative, it is not just the re-observed features which improve but also other previously viewed regions, as the affect of the improved estimates propagates throughout the model. This propagation/convergence, means that lo- calised maps provide for high levels of accuracy in terms of relative measurements within a model, however they can lack accuracy with regards to where they be- lieve they, and the terrain, fit on a larger scale, with Figure 2.11 demonstrating this characteristic. Potentially, scanning matching with maps from other robots maybe necessary to reconcile global positioning (as mentioned in swarms/ad-hoc networks). An issue which can be present when using relative positioning to build maps, is that the environment may alter over time and the disparity between the held model and real terrain can flow through to affect the robot’s localisation. Using a probabilistic approach allows for variance and subsequently adjustment over time, allowing the model and localisation to eventually converge with the (newly altered) real terrain. For non-SLAM based systems it may be necessary to instead discard all the data and begin again or to have some measure of tracking trust/confidence about regions.
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MME-EKF-Based Path-Tracking Control of Autonomous Vehicles Considering Input Saturation

MME-EKF-Based Path-Tracking Control of Autonomous Vehicles Considering Input Saturation

Jing Na (M’ 15) received the B.Eng. and Ph.D. degrees in control engineering from the School of Automation, Beijing Institute of Technology, Beijing, China, in 2004 and 2010, respectively. From 2011 to 2013, he was a Monaco/ITER Post- Doctoral Fellow with ITER Organization, Saint- Paul-l`es-Durance, France. From 2015 to 2017, he was a Marie Curie Intra-European Fellow with the Department of Mechanical Engineering, University of Bristol, Bristol, U.K. Since 2010, he has been with the Faculty of Mechanical and Electrical Engi- neering, Kunming University of Science and Technology, Kunming, China, where he became a Full Professor in 2013. He has co-authored one monograph published in Elsevier and authored or co-authored over 100 international jour- nal and conference papers. His current research interests include intelligent control, adaptive parameter estimation, nonlinear control and applications for robotics, vehicle systems, and wave energy convertor. Dr. Na was a recipient of the Marie Curie Fellowship from EU, the Best Application Paper Award of the 3rd IFAC International Conference on Intelligent Control and Automation Science in 2013, and the 2017 Hsue-Shen Tsien Paper Award. He is currently an Associate Editor of Neurocomputing and has served as the IPC Chair of ICMIC in 2017.
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A driver-vehicle model for impaired motorists and strategies for planning autonomous vehicles

A driver-vehicle model for impaired motorists and strategies for planning autonomous vehicles

controller where the controlled element is the vehicle model. The simple transfer functions may represent the driving strategy for diminishing the lateral deviation and previewing the desired path. The cross-over model [16-20] was used to identify the driver’s parameters such as the reaction time representing the neuromuscular properties and gains, time lead and lag associating with the controlled vehicle. Another approach to the closed-loop driver-vehicle system was the application of the optimal preview control [26-29]. The steering angle is obtained by the steering control strategy that can be viewed as a time-lagged optimal control process. The multi-point preview was introduced to incorporate the future path information as the system states [32, 35]. Two components of gains, the vehicle prediction and path preview, are determined by the linear quadratic regulator method.
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A multiobjective optimization approach for COLREGs-compliant path planning of autonomous surface vehicles verified on networked bridge simulators

A multiobjective optimization approach for COLREGs-compliant path planning of autonomous surface vehicles verified on networked bridge simulators

In practice, depending on mission requirements, a number of objectives/preferences such as safety, efficiency, short-distance and comfort, may need to be considered when planning a path for ASVs [5]–[7]. For instance, it is desired that an ASV for mine counter measure operations does not deviate far from pre-specified areas, and an ASV used as cruise ship should manoeuvre smoothly without abrupt course/speed changes. Nonetheless, it should be noted that besides all the mentioned objectives, collision avoidance should always be considered as a hard constraint that should be satisfied during path planning. In addition, from a good seamanship viewpoint, an evasive manoeuvre with course changes alone is always preferred over one involving speed changes. The simple reason is that course changes are much more visible to a naked eye than speed changes. How to address the above-mentioned multiple objectives, and more importantly to take into account the priorities among all objectives and good seamanship in the path planner constitute the second motivation of our research in this paper.
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