Top PDF High-Speed Obstacle Avoidance at the Dynamic Limits for Autonomous Ground Vehicles

High-Speed Obstacle Avoidance at the Dynamic Limits for Autonomous Ground Vehicles

High-Speed Obstacle Avoidance at the Dynamic Limits for Autonomous Ground Vehicles

AGVs at high speed through unstructured environments without collision while en- suring vehicle dynamical safety. However, the formulation assumes that the vehicle longitudinal speed is maintained constant, which can limit the mobility performance and the obstacle fields that can be cleared with this algorithm. Thus, the formulation is later extended to simultaneously optimize both the longitudinal speed and steering control commands. The novelty of the formulation includes: (1) A varying prediction horizon MPC is used to achieve a fixed distance prediction. This is prompted by two features of the proposed system. First, the terminal point of the planned trajectory is constrained at the LIDAR ’s maximum detection range in an effort to fully utilize as much information from the LIDAR as possible. Second, the variable vehicle speed necessarily leads to a variable prediction horizon with the previous constraint. (2) The effects of powertrain and brake systems are taken into account through the relation- ship between acceleration and speed and the bounds on longitudinal jerk, acceleration, and speed. The vehicle’s acceleration capability varies with the speed resulting from the powertrain and brake systems. To generate a speed profile that can be tracked by the vehicle, the algorithm uses an offline generated look-up table to account for the acceleration and deceleration limitations. (3) The no-wheel-lift-off requirement is considered through both hard and soft constraints using equations with empirical parameters that can predict tire vertical loads. A hard constraint bounds the vertical loads to be greater than a specified minimum threshold. A soft constraint is also used to provide a smooth approach to this threshold to prevent overshoot. Three sets of numerical simulations are conducted to demonstrate the effectiveness of the algorithm.
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On-board Obstacle Avoidance in the Teleoperation of Unmanned Aerial Vehicles

On-board Obstacle Avoidance in the Teleoperation of Unmanned Aerial Vehicles

Operation in unstructured environments is arguably one of the largest impedi- ments to full autonomy, with additional challenges arising from close interaction, noise and ambiguity in sensing. Variability of consecutive cases (case-to-case vari- ability) of a given application leads to a lack of generalization, which in turn trans- lates to a difficulty in categorization and in defining robot behaviors. For example, in applications such as medical robotics, or the decommissioning of a nuclear plant, the great variability between cases makes defining robot behaviors extremely hard. A trade-off between high reliability in teleoperation and autonomous systems’ ef- ficiency can be achieved through the concept of shared autonomy. Such a resolution may be also desired as a compromise in situations when legal limits are imposed on the use of autonomous systems or because of practical reasons. By enabling certain autonomous behaviors the system’s performance can be increased while si- multaneously reducing the operator’s mental workload. To achieve this, however, it is also important to maintain good situational awareness as operator throughout the whole process. The operator should be well aware of any autonomous behavior of the platform and be able to supervise it, such that their decision making and supervision is not limited.
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Flow-field guided steering control for rigid autonomous ground vehicles in low-speed manoeuvring

Flow-field guided steering control for rigid autonomous ground vehicles in low-speed manoeuvring

The filled contours represent the divergency field. In Figure 4 (a), fluid is converg- ing before the obstacle around (28 m, 6 m), and diverging after it. The vehicle has gone through the region of high divergency around (37 m, 6 m). High divergency literally offers poor guidance, since it provides inconsistent direction. Fortunately this is not a branch- ing situation, the vehicle will eventually turn to the correct direction just by following the result of the least squares method. When the inlet speed of the fluid is high, as shown in Figure 4 (b), the pushing of the recirculation area makes the flow outside the recir- culation area more convergent, which is actually beneficial from the perspective of the divergency field. In Figure 4 (c), the absence of wall friction reduces the fluid’s ability to spread the information of obstacles, making the converging and diverging region closer to the boundaries and corners. Hence the flow quality for fitting the rigid vehicle motion is somewhat improved.
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Obstacle avoidance for an autonomous vehicle using force field method

Obstacle avoidance for an autonomous vehicle using force field method

In recent decades advanced technologies for active vehicle safety have become very popular and implemented in road vehicles. Automatic or assisted braking, adaptive cruise control, lane departure warning, and collision warning systems are among many available safety options [1]. New options such as lane departure control, active steering, automatic lane change and automatic collision avoidance, reveals the need for active systems to perform the required maneuvers to avoid a crash. Such maneuvers require two main functions: a planned trajectory for the vehicle to follow and a controller to guide the vehicle to safely traverse the planned trajectory. When there is an eminent collision situation, the vehicle often has a high speed and integrated lateral- longitudinal control motion needs to be performed in a very short period of time.
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Levenberg-Marquardt optimised neural networks for

trajectory tracking of autonomous ground vehicles

Levenberg-Marquardt optimised neural networks for trajectory tracking of autonomous ground vehicles

Abstract: Trajectory tracking is an essential capability of robotics operation in industrial automation. In this article, an artificial neural controller is proposed to tackle trajectory-tracking problem of an autonomous ground vehicle (AGV). The controller is implemented based on fractional order proportional integral derivative (FOPID) control that was already designed in an earlier work. A non-holonomic model type of AGV is analysed and presented. The model includes the kinematic, dynamic characteristics and the actuation system of the VGA. The artificial neural controller consists of two artificial neural networks (ANNs) that are designed to control the inputs of the AGV. In order to train the two artificial neural networks, Levenberg-Marquardt (LM) algorithm was used to obtain the parameters of the ANNs. The validation of the proposed controller has been verified through a given reference trajectory. The obtained results show a considerable improvement in term of minimising trajectory tracking error over the FOPID controller.
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Connected Autonomous Vehicles: Capacity Analysis, Trajectory Optimization, and Speed Harmonization

Connected Autonomous Vehicles: Capacity Analysis, Trajectory Optimization, and Speed Harmonization

It is difficult to solve PTO to the exact optimal due to infinite-dimensional vari- ables, highly nonlinear objective and vehicle dependency in the constraints. By adapting the simplification approach in Ma et al. (2017), instead of directly analyzing PTO, this study formulates a simplified model that restricts each trajectory to consist of no more than five quadratic segments and trajectories in each platoon (platoon will be defined in the next paragraph) have identical acceleration and deceleration magnitudes. Although this restric- tion may slightly sacrifice the solution optimality since it reduces the feasible region of the trajectories, we believe that the restricted solution shall be close to the true optimum for the following two reasons. First, since a realistic vehicle cost function (4.12) shall be optimal with a smooth vehicle trajectory that does not frequently decelerate and accelerate, using piecewise quadratic approximation shall not bring too much error to the optimal trajectory shape. Second, since these vehicles closely follow each other in a platoon, they optimal trajec- tories shall have similar acceleration and deceleration levels, and thus assuming them sharing identical acceleration and deceleration magnitudes will not much compromise the optimality. While it is interesting to verify this conjecture with theoretical analysis and numerical exper- iments, it is beyond the scope of this study. This study will focus on formulating, analyzing and testing this simplified model, and this section will present the formulation of this model. First, we want to note that safety constraints (4.7) will not be activated for two consecutive vehicles n and n + 1 if vehicle n’s departure time is not much later than vehicle
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Speed Control of Unmanned Ground Vehicle for Non Autonomous Operation

Speed Control of Unmanned Ground Vehicle for Non Autonomous Operation

The automated drive train is shown schematically in Fig. 6 It consists of the engine with a open loop speed controller and two sets of hydraulic pump-motor systems each with a closed-loop wheel speed controller. The hydraulic pumps act as a load disturbance on the engine since they must overcome inertia and drag on the vehicle. The engine acts as a speed disturbance to both pumps, since the wheel speed is directly proportional to engine speed. Fortunately, only a slight change to the low-level control architecture is required to decouple the three systems. Relying on the drive controllers exclusively to govern the speed of the wheels results in incorrect responses to terrain variation disturbances. This problem provides another compelling reason for decoupling the low-level control loops in the manner described above. When the Vehicle encounters a hill, the added load reduces the vehicle's speed. Wheel speed drive controllers would respond by moving the swash levers toward higher displacement, since this should result in higher wheel speed and compensate for the disturbance. Actually, this is analogous to shifting an automobile into a higher gear for driving up a steep
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Person Classification Leveraging Convolutional Neural Network for Obstacle Avoidance via Unmanned Aerial Vehicles

Person Classification Leveraging Convolutional Neural Network for Obstacle Avoidance via Unmanned Aerial Vehicles

Although classical approaches on computer vision has been popular and dominating for decades in providing classi- fication solution, it is still suffering from robustness problems [2]. In terms of methods that utilize CNN for classification task, our approach is similar to [2] but different in the sense that our work uses visible images while their work uses thermal input data. In [3], they solve classification problem by using CNN for high speed vehicle (~300 fps) which is rather different from our speed requirement. We instead define our speed requirement to be somewhat in decent time (~5 fps) due to the fact that indoor exploration does not require a fast vehicle motion.
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Autonomous Speed Control of Over Speeding Vehicles Using Radio Frequency

Autonomous Speed Control of Over Speeding Vehicles Using Radio Frequency

ABSTRACT: This paper presents Autonomous speed control of over speeding vehicle using Radio Frequency. The main objective is to design a controller and a display, meant for vehicle’s speed control and to monitor the zones, which can run on an embedded system. Display & Control can be custom designed to fit into a vehicle’s dashboard, and display information on the vehicle. The proposed system is composed of two separate units: Zone status transmitter unit and receiver (speed display and control) unit. Whenever the vehicle is within the transmitter zone, the vehicle speed is controlled by receiving the signal, i.e.., every time the vehicle speed is decreased by some cut off and kept constant until the vehicle moves out of the transmitter zone, and then the vehicle can get accelerated by itself. The IR sensor detects the speed of the vehicle and sends the information to Micro controller. Micro controller interacts with motors through driver IC to take appropriate directions to prevent accidents.
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Autonomous high-speed overtaking in structured environments

Autonomous high-speed overtaking in structured environments

The reference points, dynamically generated, are used by the Trajectory Gen- eration block in Figure 3.4 as described in Section 3.7 . The results from the entire simulation are depicted in Figure 3.8 . The trajectories of the subject and lead vehicles as well as the relevant states and inputs of the subject vehicle are shown in the inertial frame in Figure 3.8 . Moreover, Gaussian noise is added to the lead vehicle’s velocity in an attempt to (i) introduce sensor imperfections, (ii) wireless network packet loss, and (iii) lack of accurate knowledge of lead-vehicle states. Introducing this noisy signal in the potential field calculation in ( 3.35 ) will help in understanding if the proposed technique is robust against the random variations in lead-vehicle states. The top plot shows the actual path followed by the subject and lead vehicles and the trajectory of the overtaking manoeuvre for the subject vehicle can be observed. Moreover, since the subject vehicle is travelling with a higher longitudinal velocity, it covers a larger portion of the road segment in the given time. The bottom four plots of Figure 3.8 show the states and input of the subject vehicle evolving over time. The key aspect about the overtaking manoeuvre is that the overtaking manoeuvre is initiated close to 10 s and one can observe the longitudinal velocity of the vehicle increasing while the first lane change manoeuvre is being performed. The reverse behaviour (i.e., decreasing velocity while performing the lane change) is visible after 20 s. This is reminiscent of a real-world overtaking manoeuvre where a vehicle may accelerate or decelerate while performing the lane change manoeuvre(s) thus demonstrating the efficacy of the proposed controller. The noisy data from the lead vehicle’s velocity also does not have any impact on the trajectory planning process as both the states and input signals are devoid of high-frequency oscillation. Another key aspect is that the two states of SV, (i) longitudinal velocity and (ii) heading-angle show smooth evolution without any high-frequency oscillation during either of the lane-changes. The longitudinal acceleration profile is obtained via the tracking controller discussed above and it also does not demonstrate any high-frequency oscillations. However, it is designed using basic frequency-based techniques and is not tuned to minimize the jerk but if required this controller can be swapped with any preferred control technique available in literature. Similarly, the steering action for the lateral motion demonstrates smooth evolution with no high-frequency oscillation. Moreover, just as in the case of the longitudinal tracking controller, if necessary another controller for the steering action can be utilized with the proposed trajectory planning framework. Also, as expected the MPC controller respects all the system and input constraints which is evident from the plots in Figure 3.8 .
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Efficient Optical flow and Stereo Vision for Velocity Estimation and Obstacle Avoidance on an Autonomous Pocket Drone

Efficient Optical flow and Stereo Vision for Velocity Estimation and Obstacle Avoidance on an Autonomous Pocket Drone

Our main contribution is that the presented method provides both velocity and distance estimates, while still being compu- tationally efficient enough to run close to the frame rate on a very limited embedded processor. As such, the method enables unstable MAVs such as tiny quadcopters to perform fully autonomous flights in unknown environments. The EdgeFlow and EdgeStereo methods will be explained in more detail in section II. Off-line results for velocity estimates with a set of images is shown in section III. From here, the algorithm is embedded on the lightweight stereo camera and placed on 40 g pocket drone for velocity estimation (section IV-B). Finally, the velocity estimate is used together with EdgeStereo-based obstacle detection to perform fully autonomous navigation in an environment with obstacles (section IV-C). This is followed by some concluding remarks.
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Remote and Autonomous Controlled Robotic Car based on Arduino with Real Time Obstacle Detection and Avoidance

Remote and Autonomous Controlled Robotic Car based on Arduino with Real Time Obstacle Detection and Avoidance

In the second mode (automatic), the user can take the robot in automatic mode by pressing the automatic mode key that the user sees in the interface of the application. In this mode, the robot will be able to control its basic movements. By means of the ultrasonic sensor, the obstacles are detected by Robot at 25 cm away. When the robot detects the obstacle, the robot stops itself and goes back 2 cm. Then the PIR sensor will activate by the robot system and detect whether the obstacle is live or not. The robot computes the radiated heat from the obstacle. The object emits heat, and if it can move, the robot finds that the obstacle is alive. If the obstacle is a living entity the buzzer will active and an audible alarm sounds. Then the robot will escape from the obstacle and will continue on way. If the obstacle it perceives is an inanimate object, it computes the distance to the farthest point away from the obstacle and continues on its way by avoiding obstacles in that direction. This process continues until the user stops the robot and exits from the application. In addition, the user can configure the buttons in the application interface as desired. The user can give the desired values to the buttons.
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Modeling the Turning Speed and Car Following Behaviors of Autonomous Vehicles in a Virtual World

Modeling the Turning Speed and Car Following Behaviors of Autonomous Vehicles in a Virtual World

The Virtual World created in order to implement the ex- periments was developed in the Unity engine; the data employed was obtained from user tests. In the vehicle turning tests cases, it is considered that the vehicle enters the curve an approximated speed of 60 km/h, and there ’œȱ—˜ȱ’—ĚžŽ—ŒŽȱ˜ȱ˜‘Ž›ȱŸŽ‘’Œ•Žœȱ’—ȱ‘Žȱ›˜—ȱ˜›ȱ‹ŠŒ”ǯȱ‘Žȱ created curves represent one quarter of the circle circum- ference since the vehicle reaches the comfortable speed before completing the circle circumference quarter. In order to obtain data in car following situations the leader vehicle accelerates gradually from zero to the maximum acceleration and then smoothly returns to cero, the same for decelerating; in the case of the following vehicle, i.e. the vehicle controlled by the user, it accelerates linearly from the maximum acceleration to zero and the same way for decelerating. The tests to obtain the data are per- ˜›–ŽȱŠœȱ˜••˜ œDZȱꛜȱ‘ŽȱŸŽ‘’Œ•ŽœȱȮ•ŽŠŽ›ȱŠ—ȱ˜••˜ - er– as initial conditions are stopped, then the leader accelerates and the follower begins to follow it until both enter in a stable regime, after that the leader begins to decelerate and the follower does the same until both ve- hicles are stopped.
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Real time implementation of obstacle avoidance for an autonomous mobile robot using monocular computer vision

Real time implementation of obstacle avoidance for an autonomous mobile robot using monocular computer vision

Image smoothing involves subjecting the image to a controlled blur, this smooths out the random high points in the image caused my noise (Bradski & Kaehler 2008). The difficulty with this, as might be imagined, is that important features can become obscured if the blur is severe enough. Initially, a standard Gaussian blur was used however it was found that the more complicated Bilateral filter would be able to perform a Gaussian blur on the interior of objects whilst pre- serving the edges, the effect of which is “typically to turn an image into what appears to be a watercolor painting of the same scene. This can be useful as an aid to segmenting the image” (Bradski & Kaehler 2008). This quote was taken at face value and a moderate Bilateral filter installed before the segmentation algorithm and run once over each frame.
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Dynamic Obstacle Avoidance by Distributed Algorithm based on Reinforcement Learning (RESEARCH NOTE)

Dynamic Obstacle Avoidance by Distributed Algorithm based on Reinforcement Learning (RESEARCH NOTE)

In this paper, we focus on the application of reinforcement learning to obstacle avoidance in dynamic environments in wireless sensor networks. A distributed algorithm based on reinforcement learning is developed for sensor networks to guide mobile robot through the dynamic obstacles. The sensor network models the danger of the area under coverage as obstacles, and has the property of adoption of itself against possible changes. The proposed protocol can integrate the reward computation of the sensors with information of the intended place of robot so that it guides the robot step by step through the sensor network by choosing the safest path in dangerous zones. Simulation results show that the mobile robot can get to the target point without colliding with any obstacle after a period of learning. Also, we discussed about time propagation between obstacle, goal, and mobile robot information. Experimental results show that our proposed method has the ability of fast adoption in real applications in wireless sensor networks.
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Obstacle Detection and Collision Avoidance System

Obstacle Detection and Collision Avoidance System

The motion planning layers is responsible computing safe, comfortable, and dynamically feasible trajectory from the vehicle current configurations to the goal configuration provide by the behavioral layer of the decision making hierarchy. Depending on context, the goal configuration may different. For example, the goal location may be the center point of the current lane a number of meters ahead in the direction of travels, the center of the stopped lines in next intersections, or the next desired parking spots. The motion planning components accepts information about statics and dynamics obstacle rounded vehicle and generate a collision-free trajectory that satisfies dynamics and kinematic constraint on the motion of vehicles. The motion planner also minimize a given objective functions. In addition to travel times, the objective function may penalize hazardous motions or motions that cause passenger discomfort. In a typical setup, the output of the motion planner is then passed to the local feedback control layers. Feedback controller generates input signals to regulated the vehicle to follow this given motion plan. A motion plan for the vehicle can take the form of a path or a trajectory. Note that such a solution does not prescribe how this path should be follow, ones can either choose a velocity profile for path or delegate this task to lower layers of the decision hierarchy. Within the trajectory planning framework. Control execution time is explicitly considered. This consideration allows for direct modeling of vehicle dynamics and dynamic obstacles.
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Capacity of High-Speed Powerline Communication in Vehicles

Capacity of High-Speed Powerline Communication in Vehicles

This idea came from the concept of in-house networking, which is a part of technology transferring data through low voltage distribution cables well-known as powerline communication. Since middle of May 2001 the commercial internet connection through wall- socket with data rate of 2 Mbps became reality. This success story tends to be adopted by transportation system, especially vehicles. Such networking method makes data cables unusable and therewith reduces the vehicles weight and production costs significantly. A new method of electric lines conditioning has given the possibility to realize high speed data transmission of 10 Mbps through copper lines.
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Investigating pedestrians’ obstacle avoidance behaviour

Investigating pedestrians’ obstacle avoidance behaviour

Abstract - Modelling and simulating pedestrian motions are standard ways to investigate crowd dynamics aimed to enhance pedestrians’ safety. Movement of people is affected by interactions with one another and with the physical environment that it may be a worthy line of research. This paper studies the impact of speed on how pedestrians respond to the obstacles (i.e. Obstacles avoidance behaviour). A field experiment was performed in which a group of people were instructed to perform some obstacles avoidance tasks at two levels of normal and high speeds. Trajectories of the participants are extracted from the video recordings for the subsequent intentions:(i) to seek out the impact of total speed, x and y- axis (ii) to observe the impact of the speed on the movement direction, x-axis, (iii) to find out the impact of speed on the lateral direction, y-axis. The results of the experiments could be used to enhance the current pedestrian simulation models.
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Object Recognition and Obstacle Avoidance Robot

Object Recognition and Obstacle Avoidance Robot

time, constantly sends messages to the SONAR module. The ultrasonic sensors of the SONAR module respond back with an acknowledgement whenever an obstacle is detected. The SONAR module also calculates the distance of the object and sends it to the master. When the distance is within the reach of the robot arm, a message is sent by the master to the base unit to apply the brakes and stop the unit. With the help of three sonar sensors, the base is aligned to be exactly in front of the object. This ensures proper focusing of the webcam to capture the image of the obstacle accurately. A frame is then captured and the image is analyzed to identify the shape and the colour of the object. If it matches the specification of our object then the arm unit is activated to pick up the ball, else the robot moves around the obstacle and continues on its path.
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Overview obstacle maps for obstacle aware navigation of autonomous drones

Overview obstacle maps for obstacle aware navigation of autonomous drones

fusing the inertial measurement unit (IMU) readings with optical flow, thus only requiring a minimal amount of feature correspondences in consecutive frames. Using this, efficient version of PTAM (Weiss, Achtelik, Kneip, Scaramuzza, & Siegwart, 2011) showed an effective terrain exploration technique for micro ‐aerial vehicles (MAVs) that generate, in real time in a ground ‐station, a textured 3D mesh by means of a Delaunay triangulation (Labatut, Pons, & Keriven, 2007), which supports the drone operator in understanding the MAV ’s environment. Several research works have focused on the creation of maps that can be later reused by the drone to localize in real time during an autonomous flight. Surber, Teixeira, and Chli (2017) use the VIO algorithm open keyframe-based visual-inertial SLAM (OKVIS) (Leuteneg- ger et al., 2013, 2015) to acquire a map of an area during a manual flight, and later reuse this map to reduce the UAV ’s dependency on global positioning system (GPS) and evaluated their system against ground ‐truth position data acquired with a Leica Total Station. Recently, researchers from the ETH Zürich have released a visual ‐inertial mapping framework to process and produce multisession maps (T. Schneider et al., 2018), which uses robust visual inertial odometry (ROVIO) (Bloesch, Omari, Hutter, & Siegwart, 2015) as the VIO front ‐end, and has been used to achieve autonomous drone flight (Burri, Oleynikova, Achtelik, & Siegwart, 2015). In Burri et al. (2015), the full bundle adjustment (BA) result and the obstacle map are generated after a manual flight and are later used in autonomous flights achieving precise indoor localization, navigation, and obstacle avoidance. The known state ‐of‐the‐art visual SLAM frameworks ORB ‐simultaneous localization and mapping (SLAM) (Mur‐Artal, Montiel, & Tardós, 2015) and ORB ‐SLAM2 (Mur‐Artal & Tardós, 2017) also provide the capability of reusing a map acquired during a previous session
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