Car-like Vehicle Research Articles

Indoor path planning and obstacle avoidance simulation

The paper is describing the work performed for choosing and designing a path planning and obstacle avoidance solution for a car-like vehicle moving in an indoor environment. The proposed algorithm first consists in providing the vehicle with a set of passing points generated by a Dijkstra path planning algorithm. The vehicle is using the points for establishing its path and is avoiding the obstacles using scanned data from a distance sensor. In a simulation environment, initial tests were performed for estimating correct values of the vehicle’s forward and angular velocities and for estimating the effect of the scanning speed on the vehicle’s behaviour. Obstacle avoidance tests were performed for identifying specific situations possible to appear due to high velocities or to low scanning speed. Even not always choosing a smooth avoidance path, the algorithm proved to find a way for avoiding the obstacle in a clear and fast enough manner.

Stable emergent formations for a swarm of autonomous car-like vehicles

A biological swarm is an ideal multi-agent system that collectively self-organizes into bounded, if not stable, formations. A mathematical model, developed appropriately from some principle of swarming, should enable one, therefore, to study formation strategies for multiple autonomous robots. In this article, based on the hypothesis that swarming is an interplay between long-range attraction and short-range repulsion between the individuals in the swarm, a planar individual-based or Lagrangian swarm model is constructed using the Direct Method of Lyapunov. While attraction ensures the swarm is cohesive, meaning that the individuals in the swarm remain close to each other at all times, repulsion ensures that the swarm is well-spaced, meaning that no two individuals in the swarm occupy the same space at the same time. Via a novel Lyapunov-like function with attractive and repulsive components, the article establishes the global existence, uniqueness, and boundedness of solutions about the centroid. This paves the way to prove that the swarm model, governed by a system of first-order ordinary differential equations (ODEs), is cohesive and well-spaced. The article goes on to show that the artificial swarm can collectively self-organize into two stable formations: (i) a constant arrangement about the centroid when the system has equilibrium points, and (ii) a highly parallel formation when the system does not have equilibrium points. Computer simulations not only illustrate these but also reveal other emergent patterns such as swirling structures and random-like walks. As an application, we tailor the model accordingly and propose new autonomous steering laws giving rise to pattern-forming for multiple nonholonomic car-like vehicles.

Combined Longitudinal and Lateral Control of Car-Like Vehicle Platooning With Extended Look-Ahead

In this paper, we present a novel look-ahead concept for combined longitudinal and lateral vehicle following control for a car-like platoon. A nonlinear controller structure, which is based on Cooperative Adaptive Cruise Control, is designed for the lateral and longitudinal direction. For practical implementation and cost efficiency, a preceding vehicle look-ahead approach is considered since it utilizes the already available information (such as preceding vehicle position, orientation, and velocity) from radar and vehicle-to-vehicle communication. However, due to the position control in the look-ahead approach, the follower vehicle may cut corners. To overcome this problem, the look-ahead is extended to a point perpendicular to the direction of the preceding vehicle, which can be viewed as a virtual preceding vehicle tracking objective. To demonstrate the effectiveness of the designed controller with the extended look-ahead approach, simulations are performed and further validated with experiments on a mobile robot platform. The results prove the effectiveness of the extended look-ahead approach.

Smooth path and velocity planning under 3D path constraints for car-like vehicles

Existing path and velocity planning methods for car-like vehicles, the paths of which are subject to constraints on the derivative of the curvature in the horizontal plane, do not accurately express the relationships among position, velocity and acceleration in 3D space. Moreover, velocity planning algorithms are efficient only when the curvature and derivative of the curvature have the same velocity demand. As efficiency and comfort are two key issues in promoting planning algorithms, in this paper, the vehicle is allowed to know the nearly shortest-length path and to set a continuous velocity and acceleration profile to track the trajectory reference while taking into account bounds on acceleration (including lateral acceleration) and jerk that are consistent with comfort. First, to construct a nearly shortest path, the 3D path surface is mapped onto the horizontal, profile and frontal planes, and a 2D path smoothing method is applied to solve the 3D path smoothing problem. This method has been used in highway design, but the theoretical understanding of its performance remains limited. This limitation is addressed from the viewpoint of 3D path smoothing in this paper. In addition, the jerk, acceleration, velocity, steering angle and steering angular acceleration profile are merged into a trajectory tracking task to provide a new velocity planning method to find the time-optimal path. Finally, the capabilities of the path and velocity planning methods within general planning schemes are also demonstrated.

Decentralized Platooning With Obstacle Avoidance for Car-Like Vehicles With Limited Sensing

In this letter, we consider the predecessor-following control problem for a platoon of car-like vehicles moving on a planar surface with cyclic obstacles. Each vehicle is equipped with an on-board camera that detects its preceding vehicle, and a laser scanner that detects the obstacles around it. Within this framework, we design a fully decentralized control scheme, in the sense that each vehicle calculates its own control signal incorporating only local information, acquired by its on-board camera and laser scanner. Collisions with obstacles, collisions between successive vehicles and connectivity breaks owing to the limited field of view of the camera as well as to visual occlusions raised by static obstacles are provably avoided. Moreover, the transient and steady-state response of the closed-loop system is a priori determined by certain designer-specified functions and is fully decoupled by the number of vehicles in the platoon and the control gains selection. Finally, a simulation study is carried out in MATLAB and Coppelia Robotics V-REP to prove the control protocol's efficiency.

A Point-to-Ray Framework for Generating Smooth Parallel Parking Maneuvers

The problem of generating smooth parallel parking maneuver for autonomous car-like vehicles is considered, wherein the vehicle moves forward from the starting point to an intermediate point and then traces a path backward to the parking position. Any error in accurately reaching the intermediate point leads to maneuver replanning and hence puts extra computational burden on the planner. Addressing the issue, this letter formulates the problem in a point-to-ray framework connecting the parking position to all points in a neighborhood of the intermediate point. Exploiting its asymptotic convergence, four parameter logistic (4PL) curve based S-shaped path is proposed as a solution with a single maneuver connecting the intermediate point neighborhood to the parking position. Inherent to the 4PL curve, the maneuver presents a continuous steering profile with zero steering angle at the ends. Parking slot collision avoidance and steering limit of the vehicle are satisfied by deducing closed-form analytic conditions on the two design parameters. Together with the robustness to intermediate point variation, comparison with existing approaches highlights computational and design advantages. The proposed approach is validated using a double-tracked Ackermann steering vehicle model.

Orchard manoeuvring strategy for a robotic bin-handling machine

Unlike a car-like vehicle manoeuvring its way in an open field, a four-wheel-independent-steered robotic machine placed in an orchard must operate in a very confined working space between tree rows. Because the machine is subject to the unique constraints of the worksite space and operation limits, multiple steering modes are often required to effectively accomplish the desired bin-handling manoeuvers. In this study, we created a multi-mode manoeuvring strategy selection method to generate strategies that can guide the robotic platform to accomplish bin handling tasks, such as correcting pose error between tree rows, entering a tree lane from the headland, and loading a bin between tree rows, effectively. The method determines the manoeuvring strategies based on the situation among four steering modes: 1) Ackermann steering, 2) coordinated four wheel steering, 3) crab steering, and 4) spinning. The study first evaluated applicable strategies and selected the best of these strategies for different bin handling scenarios. Then, the selected strategies were implemented to drive a four-wheel-independent-steering (4WIS) system to complete the tasks in a commercial orchard in order to validate the method. Obtained results showed that the system could navigate the platform on desired trajectories to complete bin-handling tasks with a root mean square errors less than 0.06 m.

Semianalytical minimum‐time solution for the optimal control of a vehicle subject to limited acceleration

SummaryA semianalytic solution of the minimum‐time optimal control problem of a carlike vehicle is herein presented. The vehicle is subject to the effect of laminar (linear) and aerodynamic (quadratic) drag, taking into account the asymmetric bounded longitudinal accelerations. This yields a nonlinear differential equation of the Riccati kind. The associated analytic solution exists in closed form, but it is numerically ill conditioned in some situations; hence, it is reformulated using asymptotic expansions that keep the numerical computations accurate and robust in all cases. Therefore, the proposed algorithm is designed to be fast and robust in sight to be the fundamental module of a more extended optimal control problem that considers a given clothoid curve as the trajectory and the presence of a constraint on the lateral acceleration of the vehicle. The numerical stability of the computation for limit values of the parameters is essential, as shown in the numerical tests.

Spline-based RRT∗ Using Piecewise Continuous Collision-checking Algorithm for Car-like Vehicles

This paper presents a path planning algorithm that can efficiently check for interference with potential obstacles while piecewise continuously computing the required space of moving car-like vehicles using cubic Bezier curves. Our collision-checking algorithm uses trajectories generated from a vehicle's front outer corner and rear inner axle, as well as partially overlapped rectangles. These outer and inner trajectories are computed from the trajectory generated by the center of the rear axle of the vehicle, which considers the dimensions of the vehicle, and the tangential and normal vectors of the trajectory. To validate the continuity and efficacy of our collision-checking algorithm, the collision-checking algorithm is applied to a spline-based RRTź, where the kinematics (or minimum turning radius) of car-like vehicles is satisfied using cubic Bezier curves. We show the benefits of our method through simulations and experimental results by using an autonomous ground vehicle.

Semi-analytical minimum time solutions with velocity constraints for trajectory following of vehicles

We consider the problem of finding an optimal manoeuvre that moves a car-like vehicle between two configurations in minimum time. We propose a two phase algorithm in which a path that joins the two points is first found by solving a geometric optimisation problem, and then the optimal manoeuvre is identified considering the system dynamics and its constraints. We make the assumption that the path is composed of a sequence of clothoids. This choice is justified by theoretical arguments, practical examples and by the existence of very efficient geometric algorithms for the computation of a path of this kind. The focus of the paper is on the computation of the optimal manoeuvre, for which we show a semi-analytical solution that can be produced in a few milliseconds on an embedded platform for a path made of one hundred segments. Our method is considerably faster than approaches based on pure numerical solutions, it is capable to detect when the optimal solution exists and, in this case, compute the optimal control. Finally, the method explicitly considers nonlinear dynamics, aerodynamic drag effect and bounds on the longitudinal and on the lateral acceleration.

A Consensus Approach to Dynamic Programming

Motivated by the finite element formulation of the Hamilton-Jacobi-Bellman (HJB) equation, we introduce a consensus algorithm to compute the solution of a class of optimization problems that can be solved with a fixed point iteration. The proposed algorithm reduces the computational cost in terms of elementary operations with respect to a complete fixed point iteration. We provide theoretical results on maximum error rate and on the convergence of the algorithm. As an application, we compute the minimum-time solution for a parking maneuver of a car-like vehicle, comparing the fixed point iteration with the consensus iteration.

Clothoids Composition Method for Smooth Path Generation of Car-Like Vehicle Navigation

This paper addresses a continuous curvature path generation problem for car-like vehicle navigation. The continuous curvature path is generated by multiple clothoids composition and parametric adjustment. According to the geometric conditions of the given initial and final configurations, the path generation problem is classified into two cases and then, each problem is solved by by appropriate proposed algorithm. The solution is obtained by iterative procedure subject to geometric constraint as well as solution constraints. For computational efficiency and fast convergence in the proposed algorithms, a minimax sharpness constraint is proposed as the solution constraint by minimizing the maximum sharpness of the feasible solutions. After the generation of the proposed path, the resultant curvature/sharpness diagram provides a useful information about its orientation and curvature continuity along the travel length. The proposed path planning strategy, permits us to obtain online, smooth and safe path between two defined configurations while ensuring high passengers comfort (continuous curvature and transition between the different composed clothoids). The algorithmic proposals have been applied to generate continuous curvature for two cases. The first correspond to local path planning for ensuring obstacle avoidance or lane change. The second application corresponds to global path smoothing. The resultant global path path is tested on the Lyapunov-based control scheme and showed improved performance on its steering work (reduction of 41.0% than the driving based on the raw data), which permits us therefore to validate the effectiveness of the obtained global path for car-like vehicles path following.

Autonomous Path Planning for Road Vehicles in Narrow Environments: An Efficient Continuous Curvature Approach

In this paper we introduce a novel method for obtaining good quality paths for autonomous road vehicles (e.g., cars or buses) in narrow environments. There are many traffic situations in urban scenarios where nontrivial maneuvering in narrow places is necessary. Navigating in cluttered parking lots or having to avoid obstacles blocking the way and finding a detour even in narrow streets are challenging, especially if the vehicle has large dimensions like a bus. We present a combined approximation-based approach to solve the path planning problem in such situations. Our approach consists of a global planner which generates a preliminary path consisting of straight and turning-in-place primitives and a local planner which is used to make the preliminary path feasible to car-like vehicles. The approximation methodology is well known in the literature; however, both components proposed in this paper differ from existing similar planning methods. The approximation process with the proposed local planner is proven to be convergent for any preliminary global paths. The resulting path has continuous curvature which renders our method well suited for application on real vehicles. Simulation experiments show that the proposed method outperforms similar approaches in terms of path quality in complicated planning tasks.

Dependence maximization localization: a novel approach to 2D street-map-based robot localization

Recently, localization methods based on detailed maps constructed using simultaneous localization and mapping have been widely used for mobile robot navigation. However, the cost of building such maps increases rapidly with expansion of the target environment. Here, we consider the problem of localization of a mobile robot based on existing 2D street maps. Although a large amount of research on this topic has been reported, the majority of the previous studies have focused on car-like vehicles that navigate on roadways; thus, the efficacy of such methods for sidewalks is not yet known. In this paper, we propose a novel localization approach that can be applied to sidewalks. Whereas roadways are typically marked, e.g. by white lines, sidewalks are not and, therefore, road boundary detection is not straightforward. Thus, obtaining exact correspondence between sensor data and a street map is complex. Our approach to overcoming this difficulty is to maximize the statistical dependence between the sensor data and the map, and localization is achieved through maximization of a mutual-information-based criterion. Our method employs a computationally efficient estimator of squared-loss mutual information, through which we achieve near real-time performance. The efficacy of our method is evaluated through localization experiments using real-world data-sets

Primitive cable car “varangel” accidents in the black sea region of Rize in Turkey

Varangels are simple and uniquely-designed cable car-like vehicles commonly used for human and cargo transportation in the Eastern Black Sea Region of Turkey, a mountainous region. Varangels cause various accidents, resulting even in death. This study investigated the characteristics of the patients with injuries caused by varangel accidents. Medical records of 78 patients (55 males) admitted with injuries due to varangel accidents to Recep Tayyip Erdogan University Training and Research Hospital between January 2010 and December 2012 were retrospectively evaluated. Data including age, gender, site and type of the injury, accident type, and outcomes were recorded. No significant differences were determined among patients in terms of the time and type of the accident, site of the injury and outcomes according to gender or age groups. According to the type of the accident, significant differences were observed regarding the site of the injury and outcomes. Compression injuries appeared to affect the extremities more, whereas head-neck injuries were encountered more often due to falling from high. While patients with impact injuries were mostly treated in the outpatient units, those with compression injuries were treated both in the inpatient and outpatient units. Although no significant difference was determined according to the injury site, two patients who died had head-neck injuries. This study evaluating cable car-related accidents in Turkey is the most comprehensive study, with the highest number of cases analyzed. Therefore, local authorities take the necessary measures in place and widely used in the region varangels is intended to provide the legal regulations.

Optimal Configuration of a Downward-Facing Monocular Camera for Visual Odometry

Background/Objectives: An accurate estimation of a robot’s pose is critical for autonomous mobile vehicles. Monocular vision-based odometry is one of the robust methods used for estimating the relative motion of vehicles by using only a stream of images acquired from a camera mounted on the vehicle. Methods: The location of the camera and its configuration can affect the quality of the localization process and the maximum permissible vehicle driving speed. In this work, the elements that can affect the permissible vehicle driving speed were recognized and the equations for this allowable speed were derived. Moreover, the optimal location and configuration of a downward-facing monocular camera that ensures the success of Visual Odometry for car-like ground vehicles in low-textured environment are presented after an extensive analysis. Findings: The results show that the suggested optimal camera configuration increases the permissible vehicle driving speed. Furthermore, the percentage of correlation mismatching between image frames is decreased as the suggested camera location avoids the negative consequences of shadows and directional sunlight that can add noise to image frames and interrupt the estimation of image pixel displacement. Application/Improvements: The suggested camera configuration could be implemented in various commercial mobile robotic applications, which utilize visual odometry for improved accuracy, efficiency and cost effectiveness.

Analytical Dynamics Based Strategy for Acceleration Control of a Car-like Vehicle Motion

The paper addresses a dynamics modeling method dedicated to control design for constrained mechanical systems. The constraints may be material, control or task based, provided that they are specified by algebraic or differential equations. Theoretical analytical and control developments are related to control acceleration and jerk for car-like vehicles. In vehicle dynamics design and control, jerk and acceleration limits are control objectives related to passenger comfort and vehicle performance. Usually, their profiles are assessed experimentally and then incorporated into vehicle controllers. The paper presents a control strategy, which enables specifying desired profiles of acceleration and jerk by constraint equations.

Steering Control Strategies for a Four-Wheel-Independent-Steering Bin Managing Robot

Abstract: Automatic steering systems are a standard function for robotic agricultural equipment. Ackerman steering is the most common type of steering mechanism on such equipment, making them perform as car-like vehicles. Because of their kinematic constraints, it is quite difficult to maneuver carlike vehicles effectively in orchards due to confined working space, constrained by physical boundaries such as tree rows and other obstacles. To remove such technical difficulties, more sophisticated steering mechanisms and steering control strategies are required for robotic agricultural equipment. In order to conveniently manage fruit bins in confined tree aisles constrained by high density tree rows, a robotic bin management system, called bin-dog system, implementable in typical Washington State tree fruit orchards has been developed. This bin-dog system adopted a four-wheel-independent-steering (4WIS) mechanism as the solution to achieve the necessary drivability and maneuverability. To provide adequate controllability to this 4WIS, a four-mode steering strategy, including Ackermann steering, active front and rear steering (AFRS), crab steering, and spinning, were designed for this bin-dog to manage fruit bins effectively in the confined orchard space. This control system makes it possible for the bin-dog system to switch between four steering modes using the most appropriate steering mode to complete all maneuvering tasks in a most effective way. To design such a control system, it is important to understand the influence of major factors, such as longitudinal speed and control gain on performance under different steering modes when tracking various paths. In this paper, a pure pursuit method was implemented using a GPS-based navigation to evaluate auto-steering performance with both Ackermann steering and AFRS modes. Field tests were conducted to assess the navigation performance using different steering strategies when tracking different paths. The results indicated that by properly selecting a steering strategy for the situation, it is possible to achieve a satisfactory path tracking performance for tracking curvy paths or completing tasks such as merging and cornering.

GNSS-, Communication-and Map-Based Control System for Initiation of a Heterogeneous Rendezvous Maneuver

Abstract: This paper demonstrates a GNSS-(Global Navigation Satellite System), communication-and map-based control system initiating a heterogeneous rendezvous maneuver between a car-like ground vehicle and a fixed-wing aircraft. The rendezvous maneuver is intended to serve as a preparation step for an autonomous landing of the aircraft on the moving ground vehicle. The long term motivation is amongst others found in energy saving potentials seen in the use of ground-based takeoff and landing (TOL) support systems. For experimental validation, model-scale vehicles are used. While the ground vehicle itself could be seen a scaled version of a larger ground vehicle with similar 2-dimensional actuation capabilities, it is also intended to be used as a mock-up for a track-based MAGLEV system, a system often proposed in literature for ground-based TOL support systems. In order to mimic the behavior of a MAGLEV track, a Virtual Track application is implemented on the ground vehicle. Within the presented setup, the set-speed of the aircraft following predefined waypoints is fixed. At the same time, the longitudinal dynamics of the ground vehicle are used to synchronize it to the aircraft. The setup proposed in this paper incorporates the use of a digital map through which synchronization is assisted and automation capabilities are extended. Experimental results are shown to evaluate the possibilities of the proposed system.

Bas Multi-Robot Formation Coordination Control For Collective Missions

The paper presents a study of a mobile multi-robot formation (MRF) control problem. The robots are nonholonomic car-like vehicles and perform their mission while coordinating the formation shape when needed. The MRF control problem consists then of tracking reshaping a formation. Control design is based upon the centralized strategy assuming that information from a leader robot can be distributed to formation members. The main motivations for the work are that control strategies designed for single robots when applied to formations become complex and grow in terms of computational time, many of new control algorithms are dedicated to different formation coordination schemes and many of them were never verified experimentally. Two control levels are considered, i.e. kinematics and dynamics. Kinematics controllers applied are the input-output linearization and the Samson algorithm. They are simple in implementation and proved good performance in controlling single car-like robots. We examine them with regard to their potential applications for MRF. The dynamics controllers are based upon the generalized programmed motion equations method used successfully for a leader-follower control strategy for a fixed shape formation. Results of simulation studies, comparisons and discussions of implemented control algorithms are presented.