Car-like Robot Research Articles

A stable analytical solution method for car-like robot trajectory tracking and optimization

In this paper, the car-like robot kinematic model trajectory tracking and control problem is revisited by exploring an optimal analytical solution which guarantees the global exponential stability of the tracking error. The problem is formulated in the form of tracking error optimization in which the quadratic errors of the position, velocity, and acceleration are minimized subject to the rear-wheel car-like robot kinematic model. The input-output linearization technique is employed to transform the nonlinear problem into a linear formulation. By using the variational approach, the analytical solution is obtained, which is guaranteed to be globally exponentially stable and is also appropriate for real-time applications. The simulation results demonstrate the validity of the proposed mechanism in generating an optimal trajectory and control inputs by evaluating the proposed method in an eight-shape tracking scenario.

Circle Tracking Control of Car-like Robots by Path-generating Regulator Using Vector field

Circle Tracking Control of Car-like Robots by Path-generating Regulator Using Vector field

Adaptive fuzzy sliding mode and indirect radial-basis-function neural network controller for trajectory tracking control of a car-like robot

The ever-growing use of various vehicles for transportation, on the one hand, and the statistics ofsoaring road accidents resulting from human error, on the other hand, reminds us of the necessity toconduct more extensive research on the design, manufacturing and control of driver-less intelligentvehicles. For the automatic control of an autonomous vehicle, we need its dynamic model, which,due to the existing uncertainties, the un-modeled dynamics and the performed simpli cations, isimpossible to determine exactly. Add to this, the external disturbances that exist on the movementpath. In this paper, two adaptive controllers have been proposed for tracking the trajectory of acar-like robot. The rst controller includes an indirect radial-basis-function neural network whoseparameters are updated online via gradient descent. The second controller is adaptively updatedonline by means of fuzzy logic. The proposed controller includes a nonlinear robust section thatuses the sliding mode method and a fuzzy logic section that updates some of the nonlinear controlparameters online. The fuzzy logic system has been designed to deal with the chattering problem inthe controller of car-like robot. In both controllers, the parameters have been determined by means ofgenetic algorithm. The obtained results indicate that even with the consideration of un-ideal effectssuch as uncertainties and external disturbances, the proposed controller has been able to performsuccessfully.

Dynamic ICSP Graph Optimization Approach for Car-Like Robot Localization in Outdoor Environments

Localization has been regarded as one of the most fundamental problems to enable a mobile robot with autonomous capabilities. Probabilistic techniques such as Kalman or Particle filtering have long been used to solve robotic localization and mapping problem. Despite their good performance in practical applications, they could suffer inconsistency problems. This paper presents an Interval Constraint Satisfaction Problem (ICSP) graph based methodology for consistent car-like robot localization in outdoor environments. The localization problem is cast into a two-stage framework: visual teach and repeat. During a teaching phase, the interval map is built when a robot navigates around the environment with GPS-support. The map is then used for real-time ego-localization as the robot repeats the path autonomously. By dynamically solving the ICSP graph via Interval Constraint Propagation (ICP) techniques, a consistent and improved localization result is obtained. Both numerical simulation results and real data set experiments are presented, showing the soundness of the proposed method in achieving consistent localization.

Symbolic control design of nonlinear systems with outputs

Formal methods have been recently used as the basis of a systematic framework to address control design of continuous or hybrid systems with specifications expressed in a logic form. However, results available in the literature assume full information of the state, or of its quantization. This information may not be available in relevant applications. In this paper, we consider the more realistic scenario where the controller cannot access the state of the plant but can only access a quantized measurement of its outputs where nonidealities of the sensing devices can be modeled. We focus on a control problem where the plant is described by a possibly unstable continuous-time nonlinear control system, the controller is dynamic, digital and quantized, and takes as input a (quantized) measurement of the output of the plant, and the specification is expressed in terms of regular languages. The solution to the control problem is based on formal methods. A finite-state system, also called symbolic model approximating the plant is first derived and then used to find the solution to the control problem. An illustrative example is provided and the symbolic control of a car-like robot is presented.

Development of an autonomous smart-parallel-parking robot for the prototype of collaborative robots

One of the main problems of the collaborative mobile robot application is to share the exact information of the robot itself and the surrounding area. Each robot needs to maintain its stability and positioning in order to achieve the target. As one of the samples of achieving the positioning task, a parallel parking problem was used in this paper. This paper used a car-like robot to do a parallel parking task. Front wheels were steered by using a connected joint and a servo motor. Meanwhile, each of the rear wheels was connected to a motor DC. Four ultrasonic sensors were used to find the distance between the robot and its surrounding (fixed in front, back, middle right, and middle of right - back side). The sensors connected to an Arduino Uno as the main microcontroller. The robot used a positioning algorithm based on the distance to nearest objects. The robot is designed only for parking on the right side of the car with an assumption there is no obstacle in the left side of the car. The experimental results confirmed that our system can solve the parallel parking problem. However, during the test, the output of the sensor was were affected by the noise from the environment. Another problem was the robot hardly to move straight because the rubber tires were not installed neatly. In the future works, the data output needs to filter and corrected and the servo degree needs to be initially corrected based on the chassis and tires angle.

Neural Network-Based Learning from Demonstration of an Autonomous Ground Robot

This paper presents and experimentally validates a concept of end-to-end imitation learning for autonomous systems by using a composite architecture of convolutional neural network (ConvNet) and Long Short Term Memory (LSTM) neural network. In particular, a spatio-temporal deep neural network is developed, which learns to imitate the policy used by a human supervisor to drive a car-like robot in a maze environment. The spatial and temporal components of the imitation model are learned by using deep convolutional network and recurrent neural network architectures, respectively. The imitation model learns the policy of a human supervisor as a function of laser light detection and ranging (LIDAR) data, which is then used in real time to drive a robot in an autonomous fashion in a laboratory setting. The performance of the proposed model for imitation learning is compared with that of several other state-of-the-art methods, reported in the machine learning literature, for spatial and temporal modeling. The learned policy is implemented on a robot using a Nvidia Jetson TX2 board which, in turn, is validated on test tracks. The proposed spatio-temporal model outperforms several other off-the-shelf machine learning techniques to learn the policy.

Assessment of the Effect of Energy Consumption on Trajectory Improvement for a Car-like Robot

SummaryReducing the energy consumed by a car-like mobile robot makes it possible to move at a lower cost, yet it takes more working time. This paper proposes an optimization algorithm for trajectories with optimal times and analyzes the consequences of restricting the energy consumed on the trajectory obtained for a car-like robot. When modeling the dynamic behavior of the vehicle, it is necessary to consider its inertial parameters, the behavior of the motor, and the basic properties of the tire in its interaction with the ground. To obtain collision-free, minimum-time trajectories quadratic sequential optimization techniques are used, where the objective function is the time taken by the robot to move between two given configurations. This is subject to constraints relating to the vehicle and tires as well as the energy consumed, which is the basis for this paper. We work with a real random distribution of consumed energy values following a normal Gaussian distribution in order to analyze its influence on the trajectories obtained by the vehicle. The energy consumed, the time taken, the maximum velocity reached, and the distance traveled are analyzed in order to characterize the properties of the trajectories obtained. The proposed algorithm has been applied to 101 examples, showing that the computational times needed to obtain the solutions are always lower than those required to realize the trajectories. The results obtained allow us to reach conclusions about the energy efficiency of the trajectories.

Tracking control of a convoy of autonomous robotic cars with a prescribed performance

This paper studies the tracking control of a convoy of multiple autonomous cars or car-like robots in the planar motion without any collision with a prescribed performance that has not been addressed sufficiently in the literature yet. Toward this end, a coordinate transformation is initially employed to transform the posture errors between each two successive cars in a group from the earth-fixed frame into their relative distance and angle errors that should satisfy some predefined transient and steady-state constraints. By employing the prescribed performance technique, a nonlinear transformation is used to transform the constrained relative distance and angle errors and to obtain an unconstrained kinematic error equation. Then, a novel kinematic controller is designed to satisfy the prescribed transient and steady-state performance specifications without any collision and any controller singularity. Next, the Dynamic Surface Control technique is effectively used to simplify the convoy controller design at the dynamic level by using a first-order filter. An adaptive neural network is used to maintain the control system robustness against modelling errors and external disturbances. The Lyapunov theory proves the stability of the convoy control system and, finally, simulation examples verify the controller performance for multiple autonomous cars in intelligent transportation applications.

Near-Optimal Path Planning for a Car-Like Robot Visiting a Set of Waypoints With Field of View Constraints

This letter considers two variants of a shortest path problem for a car-like robot visiting a set of waypoints. The sequence of waypoints to be visited is specified in the first variant while the robot is allowed to visit the waypoints in any sequence in the second variant. The shortest path problem is first solved for two waypoints with heading angle constraints at the waypoints using the Pontryagin's minimum principle. Using the results for the two point problem, tight lower and upper bounds on the length of the shortest path are developed for visiting $n$ points by relaxing the requirement that the arrival angle must be equal to the departure angle of the robot at each waypoint. Theoretical bounds are also provided on the length of the feasible solutions obtained by the proposed algorithm. Simulation results verify the performance of the bounds for instances with 20 waypoints.

Efficient trajectory of a car-like mobile robot

Purpose The purpose is to create an algorithm that optimizes the trajectories that an autonomous vehicle must follow to reduce its energy consumption and reduce the emission of greenhouse gases. Design/methodology/approach An algorithm is presented that respects the dynamic constraints of the robot, including the characteristics of power delivery by the motor, the behaviour of the tires and the basic inertial parameters. Using quadratic sequential programming with distributed and non-monotonous search direction (Quadratic Programming Algorithm with Distributed and Non-Monotone Line Search), an optimization algorithm proposed and developed by Professor K. Schittkowski is implemented. Findings Relations between important operating variables have been obtained, such as the evolution of the autonomous vehicle’s velocity, the driving torque supplied by the engine and the forces acting on the tires. In a subsequent analysis, the aim is to analyse the relationship between trajectory made and energy consumed and calculate the reduction of greenhouse gas emissions. Also this method has been checked against another different methodology commented on in the references. Research limitations/implications The main limitation comes from the modelling that has been done. As greater is the mechanical systems analysed, more simplifying hypotheses should be introduced to solve the corresponding equations with the current computers. However, the solutions are obtained and they can be used qualitatively to draw conclusions. Practical implications One main objective is to obtain guidelines to reduce greenhouse gas emissions by reducing energy consumption in the realization of autonomous vehicles’ trajectories. The first step to achieve that is to obtain a good model of the autonomous vehicle that takes into account not only its kinematics but also its dynamic properties, and to propose an optimization process that allows to minimize the energy consumed. In this paper, important relationships between work variables have been obtained. Social implications The idea is to be friendly with nature and the environment. This algorithm can help by reducing an instance of greenhouse gases. Originality/value Originality comes from the fact that we not only look for the autonomous vehicle’s modelling, the simulation of its motion and the analysis of its working parameters, but also try to obtain from its working those guidelines that are useful to reduce the energy consumed and the contamination capability of these autonomous vehicles or car-like robots.

A new approach to the kinematic modeling of a three-dimensional car-like robot with differential drive using computational mechanics

This article presents a kinematic analysis of a four-wheeled mobile robot in three-dimensions, introducing computational mechanics. The novelty lies in (1) the type of robot that is analyzed, which has been scarcely dealt with in the literature, and (2) the methodology used which enables the systematic implementation of kinematic algorithms using the computer. The mobile robot has four wheels, four rockers (like an All-Terrain Mobile Robot), and a main body. It also has two actuators and uses a drive mechanism known as differential drive (like those of a slip/skid mobile robot). We characterize the mobile robot as a set of kinematic closed chains with rotational pairs between links and a higher contact pair between the wheels and the terrain. Then, a set of generalized coordinates are chosen and the constraint equations are established. A new concept named “driving modes” has been introduced because some of the constraint equations are derived from these. The kinematics is the first step in solving the dynamics of this robot in order to set a control algorithm for an autonomous car-like robot. This methodology has been successfully applied to a real mobile robot, “Robotnik,” and the results are analyzed.

Avrora Unior Car-like Robot in Gazebo Environment

Avrora Unior Car-like Robot in Gazebo Environment

An approach integrating planning and image-based visual servo control for road following and moving obstacles avoidance

ABSTRACT This paper proposes an approach that integrates planning and image based visual servo control for road following and moving obstacle avoidance. One main objective of this article is to represent a robot's general plan or strategy in the form of a finite state machine or automaton. This automaton is designed previously to execution of the plan and then it is used for any instance of the robot's task. The visual servo control is used to regulate the robot's velocity according to the visual target (task specification) which depends on the state in the automaton. All the algorithms and control laws have been implemented and simulation results and experiments with a real scale-size car-like robot are presented and discussed.

Intelligent mobile robot navigation using a neuro-fuzzy approach

This paper introduces an intelligent navigation system allowing a car-like robot to attain its destination autonomously, intelligently and safely. Based on a neuro-fuzzy (FNN) approach, the applied technique permits the robot to avoid all encountered obstacles and seek for its target's location in a local manner referring to the concepts of learning and adaptation. It uses two fuzzy Artmap neural networks, a reinforcement trial and error neural network and a Mamdani fuzzy logic controller (FLC). Experimental results in the Generator of modules (GenoM) robotics architecture, in an unknown environment, shows the FNN effectiveness for the autonomous mobile robot Robucar.

Intelligent mobile robot navigation using a neuro-fuzzy approach

This paper introduces an intelligent navigation system allowing a car-like robot to attain its destination autonomously, intelligently and safely. Based on a neuro-fuzzy (FNN) approach, the applied technique permits the robot to avoid all encountered obstacles and seek for its target's location in a local manner referring to the concepts of learning and adaptation. It uses two fuzzy Artmap neural networks, a reinforcement trial and error neural network and a Mamdani fuzzy logic controller (FLC). Experimental results in the Generator of modules (GenoM) robotics architecture, in an unknown environment, shows the FNN effectiveness for the autonomous mobile robot Robucar.

Control structure for a car-like robot using artificial neural networks and genetic algorithms

The idea of improving human’s life quality by making life more comfortable and easy is nowadays possible using current technologies and techniques to solve complex daily problems. The presented idea in this work proposes a control strategy for autonomous robotic systems, specifically car-like robots. The main objective of this work is the development of a reactive navigation controller by means of obstacles avoidance and position control to reach a desired position in an unknown environment. This research goal was achieved by the integration of potential fields and neuroevolution controllers. The neuro-evolutionary controller was designed using the (NEAT) algorithm “Neuroevolution of Augmented Topologies” and trained using a designed training environment. The methodology used allowed the vehicle to reach a certain level of autonomy, obtaining a stable controller that includes kinematic and dynamic considerations. The obtained results showed significant improvements compared to the comparison work.

Formation control for car-like mobile robots using front-wheel driving and steering

To solve the problem of front wheels being jammed due to the passive trajectory tracking of the conventional car-like robot in the leader–follower formation control, we propose a novel car-like robot with the integration of front-wheel driving and steering. We establish its kinematic model, then analyze its controllability via the method of chained form system, and design the trajectory-tracking controller via the backstepping method. Simulations and experimental results validate our algorithm. This novel car-like robot with the integration of front-wheel driving and steering system not only avoids the jamming in the formation motion, but also owes the advantages of compacter structure, lighter body, and lower energy consumption.

Using Fuzzy PD Controllers for Soft Motions in a Car-like Robot

Using Fuzzy PD Controllers for Soft Motions in a Car-like Robot

Driving Control Considering Passable Point for Car-like Robots By Path-generating Regulator

Driving Control Considering Passable Point for Car-like Robots By Path-generating Regulator