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A solution to the problem of controlling a car-like nonholonomic robot is proposed using a "virtual" vehicle approach, which is shown to be robust with respect to errors and disturbances. The proposed algorithms are model independent, and the stability analysis is done using a dynamical model, in which, for instance, the side slip angles are taken...
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... the single track model has its limitations, in a low speed scenario like in our application, it should suuce. If we group the front and the rear wheels together as one single wheel (single track), and let f x and f y be the forces acting on the center of gravity of the car, and m z be the torque, we get a vehicle model that can be seen in Figure 2, where ...
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... Finally, a simplified 2 DOF non-linear state space representation of the lateral dynamics of the vehicle can be expressed as follows [35,19]: ...
... 19 establishes that when the vehicle is traveling along the desired path, ψ(t) = ψ t (t), resulting in e(t) = 0. Conversely, when the vehicle deviates from the desired path, ψ(t) differs from ψ t (t), and consequently, the cross-track error, e(t), increases. In general, an increase in the difference between the target yaw angle and the current yaw angle leads to a proportional increase in the cross ...
The rapid development of autonomous vehicles has yielded significant advancements and benefits for the transportation industry. However, despite the extensive progress in autonomous vehicle technology, the emphasis has primarily been on improving vehicular autonomy rather than addressing security concerns. As a result, autonomous vehicles remain vulnerable to various cyber threats and sensor attacks. This thesis seeks to bridge this gap by developing an Intrusion Detection System (IDS) based on behavior analysis of an unmanned autonomous ground vehicle. The primary objective is to design and implement a comprehensive IDS capable of accurately detecting the attacks targeted at the perception system of autonomous vehicles. To achieve this, the thesis introduces a framework based on Anomaly Behavior Analysis that relies on temporal features extracted from a physics-based autonomous vehicle model to capture the normal static and dynamic behaviors of vehicular perception. It employs a combination of model-based techniques and machine-learning algorithms to distinguish between normal and abnormal behaviors. The study focuses on two specific perception system attacks: Global Positioning System (GPS) spoofing and depth camera blinding. These attacks were performed on autonomous vehicle testbeds, enabling the collection of real-world vehicular data that encompasses both normal and abnormal behaviors. Using these datasets, the anomaly-based perception attack detection system is developed and evaluated. Unlike the existing approach of utilizing separate IDSs to detect the perception system attacks, our proposed anomaly-based IDS exhibits a high level of accuracy in detecting both attack types, thereby reducing system complexity. Furthermore, the datasets generated during this research are the first of their kind and have been made publicly available for the research community to assess their IDSs effectively.
... In Figure 1 the levels are depict from conventional manual driving-no driving automation (SAE Level 0)-through conditional automated driving In the intermediate steps, the human-machine interaction becomes an indispensable factor for the technological progress in the development [2,3], as it acts as a technological enabler that improves performance, safety, trustworthiness and comfort [4]. This is reflected by many studies concerning the interaction of humans with vehicle controls [5][6][7]. As there is a significant probability of harming the study participants, many studies are shifted from the real driving experience on the road into the virtual driving simulator environment, with particular attention on the selection of the appropriate simulator fidelity and validity [8]. ...
As vehicle driving evolves from human-controlled to autonomous, human–machine interaction ensures intuitive usage as well as the feedback from vehicle occupants to the machine for optimising controls. The feedback also improves understanding of the user satisfaction with the system behaviour, which is crucial for determining user trust and, hence, the acceptance of the new functionalities that aim to improve mobility solutions and increase road safety. Trust and acceptance are potentially the crucial parameters for determining the success of autonomous driving deployment in wider society. Hence, there is a need to define appropriate and measurable parameters to be able to quantify trust and acceptance in a physically safe environment using dependable methods. This study seeks to support technical developments and data gathering with psychology to determine the degree to which humans trust automated driving functionalities. The primary aim is to define if the usage of an advanced driving simulator can improve consumer trust and acceptance of driving automation through tailor-made studies. We also seek to measure significant differences in responses from different demographic groups. The study employs tailor-made driving scenarios to gather feedback on trust, usability and user workload of 55 participants monitoring the vehicle behaviour and environment during the automated drive. Participants’ subjective ratings are gathered before and after the simulator session. Results show a significant increase in trust ensuing the exposure to the driving automation functionalities. We quantify this increase resulting from the usage of the driving simulator. Those less experienced with driving automation show a higher increase in trust and, therefore, profit more from the exercise. This appears to be linked to the demanded participant workload, as we establish a link between workload and trust. The findings provide a noteworthy contribution to quantifying the method of evaluating and ensuring user acceptance of driving automation. It is only through the increase of trust and consequent improvement of user acceptance that the introduction of the driving automation into wider society will be a guaranteed success.
... Recently, there have been a few studies on human-machine interaction applied to autonomous vehicles [1,2]. An advanced driver assistance system (ADAS) is a system that assists drivers in driving in various ways. ...
Currently, the existing vehicle-centric semi-autonomous driving modules do not consider the driver's situation and emotions. In an autonomous driving environment, when changing to manual driving, human–machine interface and advanced driver assistance systems (ADAS) are essential to assist vehicle driving. This study proposes a human–machine interface that considers the driver's situation and emotions to enhance the ADAS. A 1D convolutional neural network model based on multimodal bio-signals is used and applied to control semi-autonomous vehicles. The possibility of semi-autonomous driving is confirmed by classifying four driving scenarios and controlling the speed of the vehicle. In the experiment, by using a driving simulator and hardware-in-the-loop simulation equipment, we confirm that the response speed of the driving assistance system is 351.75 ms and the system recognizes four scenarios and eight emotions through bio-signal data.
... In the recent literature, see e.g. (Beard & Hadaegh, 2000;Carelli et al., 2006;Consolin et al., 2006;Do & Pan, 2007;Takahashi et al., 2004), three different approaches towards the cooperative formation control of mobile robots are described, the behavior-based approach, the leader-follower approach and the virtual structure approach: In the leader-follower approach Egerstedt et al., 1998), one of the robots is designated as the leader, with the rest being followers. Among all the approaches of formation control reported in the literature, the leader-follower method has been adopted by many researchers (Balch & Arkin, 1998;Bishop & Spong, 1998;Desai, 1998;Egerstedt et al., 2001;Frezza, 1998). ...
In this thesis, it concentrated the multi robot team navigating in an unknown environment. In our multi robot team, there is a humanoid robot as a leader and a team of two-wheel nonholonomic robots which form a vertical formation. Besides, a top camera and a computer which is a supervisor are the auxiliary robots in the multi robot team. The main purpose of the thesis is to propose an online and an offline navigation strategy for the closed and open area respectively. The core of navigation strategies is the same and it included path planning part and control part. Both the two parts constructed on the virtual structure of the formation robot team. In the former part, it improved the path planning part by the reinforcement Q learning and the image processing to acknowledge the unknown environment. And it applied the Adaptive Neural Fuzzy Inference System (ANFIS) algorithm to control of both the single nonholonomic robot and formation robot team. Furthermore, the strategies are applied to the formation robot team and the multi robot team in both closed and open environment. Simulations and real experiments are provided in the detail in the thesis. The strong points of the contribution are :- Proposition, conception, realization and experimental validation of machine learning based adaptive control for a nonholonomic single robot (in a group of robots). Four international publications have valorized this part of the doctoral Works. - Proposition, conception, realization and experimental validation of an adaptive intelligent control strategy hybridizing Artificial vision and Machine Learning for a group of nonholonomic homogeneous robots. Two international publications have valorized this part of the doctoral Works.- Proposition, conception, realization and experimental validation of an adaptive intelligent control strategy hybridizing Artificial vision and Machine Learning for a group of heterogeneous robots. Two international publications have valorized this part of the doctoral Works. It is pertinent to emphasize the investigations relative to the above-mentioned works have been awarded by: ″Innovation Award 2011″ of Industrial Robot
... Since then, different control methods have been examined, including adaptive backstepping [5], [7], [14], discontinuous backstepping [24], dynamic feedback linearization [20], approximate feedback linearization [15], and sliding mode control [6]. More recently, research has focused on the robustness of controllers to unmodeled dynamics and parameter uncertainty [9], [10], [23]. However, little research has been conducted in order to include the dynamics of the actuators in the controller design process [2], [4], [17]. ...
This paper defines a new class of systems and presents a novel controller synthesis method. This new methodology is motivated by and applied to the problem of path following of a wheeled mobile robot (WMR). The new class of systems proposed in this paper is called piecewise-afflne parameter- varying (PWAPV), which is a combination of piecewise-afflne (PWA) and linear parameter-varying (LPV) systems. The synthesis of PWAPV controllers for uncertain PWAPV systems can be cast as a parameterized set of matrix inequalities, which can be approximated by a finite set of LMIs and solved efficiently using available software. As an application, actuator input voltage laws are designed to guarantee that a WMR follows a desired path that is parameterized by a time-varying curvature. Simulation results show the effectiveness of the new control law.
... Stabilization of nonholonomic systems in obstacle-free environments has been studied in depth [4], [6], [9]. Car-like S. R. Lindemann is with the Department of Electrical Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA This work was funded in part by an NSF Graduate Research Fellowship and NSF Award 0535007 robots are common and have received significant attention [10], [21], [22], [26], [27]. Important research goals include stabilization to a point or trajectory and path following. ...
We introduce a method for constructing provably safe smooth feedback laws for car-like robots in obstacle-cluttered polygonal environments. The robot is taken to be a point with motion that must satisfy bounded path curvature constraints. We construct a global feedback plan (or control policy) by partitioning the environment into convex cells, computing a discrete plan on the resulting cell complex, and generating local control laws on the state space that are safe, consistent with the high level plan, and satisfy smoothness conditions. The trajectories of the resulting global feedback plan are smooth and stabilize the position of the robot in the plane, neglecting the orientation.
... 8). When modeling stable, non-slip conditions with relatively low accelerations, the lateral force magnitude is simply the product of the slip angle and cornering stiffness (see e.g., [15]), ...
In this paper, we describe the mathematics and computer implementation of a robotic rat pup simulation. Our goal is to understand neurobehavioral principles in a mammalian model organism—the Norway rat pup (Rattus norvegicus). Our approach is unique in that animal, simulation, and robot studies occur in parallel and inform each other. Behavior is dependent on the nervous system, body morphology, physiology, environment, and the interactions among these elements. Autonomous robotics hardware models and their associated simulations allow the possibility of systematically manipulating variables in each of these elements in ways that would be impossible using live animals. Specifically, we describe the development and validation of a Newtonian-dynamics-based simulation of a robotic rat pup, including mathematical formulation and computer implementation. The computer simulation consists of three distinct components that interact to simulate robotic behavior: (1) dynamics of the robotic rat pup itself, including sensors and actuators, (2) environmental coupling dynamics of the robot arena with the robotic rat pup, and (3) the robot control algorithms as implemented on the physical robot. The mathematical formulation, software implementation, model identification, model validation, and an application example are all described.
... The work of Sarkar [3] was essential to the development of our research. Many others were motivated by Sarkar's work, as, for example, Egersted [16], who uses an approach that considers a "virtual" vehicle in such a way that the time evolution of the reference point is governed by a differential equation which contains the position error, making possible robustness against measurement errors and external disturbances. ...
... The stability of the internal dynamics of a WMR in motion was analyzed by Yun et al. [27], concluding that the internal dynamics when the mobile robot moves forward are stable, but unstable when it moves backward. It can be stated that a system like (16), with nonholonomic constraints, is not input-state linearizable, and it may be input-output linearizable if a proper set of output equations are chosen [22]. Two appropriate output variables for the path following, h 1 and h 2 , are chosen, where h 1 is defined as the shortest distance from the control point (P l in Fig. 1) to the desired path, and h2 is defined as the forward velocity (of P l along the X axis) [3] ...
... In [15], global tracking controllers are proposed for nonholonomic wheeled mobile robots. For chained systems, tracking controllers are proposed in [8] and [16]. The dynamic tracking-control problem of the nonholonomic system has received attention recently, because most practical nonholonomic mechanical systems are dynamic systems and have uncertainties. ...
This paper presents, in detail, the implementation of a new control strategy, Kalman-based active observer controller (AOB), for the path following of wheeled mobile robots (WMRs) subject to nonholonomic constraints. This control strategy presents some particularities as being used in discrete mode, and being robust against uncertainties and disturbances such as the ones due to the use of the input-output feedback-linearization method in discrete mode, while it was developed to be used in continuous mode. The performance of the proposed control algorithm is verified via computer simulation, and is compared with other control strategies, such as pole placement controller (PPC) and PPC with a Kalman filter observer (CKF).
... For comparison, the group control input T a of the Lee-approach is transformed back to the level of the individual systems. 1 If it is assumed that q ri from the second equation is equal to q d i from the first equation in (4.13) the two control inputs are in fact exactly equal, except for the difference between C i ( q i ,˙ q i )˙ q i and C i (q i ,q i )q ri , which is structurally unimportant. Rodriquez-Angeles et al. have decided to use the Coriolis matrices of the systems in their synchronization error dynamics (see (4.5)), where Lee et al. choose to fully compensate these Coriolis matrices resulting into that no system matrices are present in the error dynamics (see (4.11)). ...
... M 2 = 5 [kg], C 1 = C 2 = 3 [kg] and g 1 = g 2 = 0 [m/s 2 ].The initial conditions are chosen as q1 (0) = −3 [m], q 2 (0) = 3 [m] andq 1 (0) =q 2 (0) = 0 [m/s] respectively and the control matrices as K p,1 = K p,2 = 10, K d,1 = K d,2 = 5 and K 1,2 = K 2,1 = 1. In figure 2.1 one can see the trajectories of q d , Mutual synchronization; time-position plot for q d ,q1 and q2 ...
... It is well known that the kinematic model of several nonholonomic systems can be transformed into a chained-form system. The global tracking problem for chained-form systems has recently been addressed in [4, 6, 7, 8, 17, 20]. In this paper we consider the tracking problem for chained form systems by means of output feedback, where we consider as output the first and last state component of the chained-form. ...
In this paper we study the tracking problem for the class of nonholonomic systems in chained-form. In particular, with the first and the last state component of the chained-form as measurable output signals, we suggest a solution for the tracking problem using output feedback by combining a time-varying state feedback controller with an observer for the chained-form system. For the stability analysis of the "certainty equivalence type" of controller we use a cascaded systems approach. The resulting closed loop system is globally K-exponentially stable.
