Power and energy consumption of skid-steer rovers turning on loose soil

This study highlights two key phenomena affecting power and energy consumption of skid-steer rovers on loose soil that is not present on the hard ground: soil excavation due to wheel counterrotation and impeded turning when dragging a braked wheel. Experiments in the field and in a controlled laboratory sandbox show that, on sand, power peaks by 15%–20% in a newly identified range of turns with radii between half the rover width, $B∕2$, to $R^{\prime }$, the radius at which the inner wheel does not turn. In this range of turns, the inner wheels rotate backwards but are being dragged forward through piles of sand they excavate by counterrotation. At $R\prime$, turns are shown to take much longer, leading to higher total energy consumption over time. Experiments in a controlled laboratory sandbox isolate the high motor torque and the resistance force experienced when a skid-steer rover drags a counterrotating or braked wheel, respectively, through loose soil. Other field experiments also demonstrate that paths combining circular arcs and lines can lead to energy savings of up to 15% relative to common ones consisting of point turns and lines; the experimental results suggest the circular arcs should have radii of approximately $2R\prime$. The quantitative values presented in this paper are specific to the rover and soils tested, but there are reasons to support the overall conclusions generalizing to all skid-steer rovers in loose soil.     

Longitudinal deep truck: Deep longitudinal model with application to sim2real deep reinforcement learning for heavy-duty truck control in the field

To develop cooperative adaptive cruise control (CACC), the choice of control approach often influences and can limit the choice of model structure, and vice versa. For heavy-duty trucks, practical application of CACC in the field is heavily influenced by the accuracy of the used model. Deep learning and deep reinforcement learning (deep-RL) have recently been used to demonstrate improved modeling and control performance for vehicles such as cars and quadrotors compared to state-of-the-art. The literature on the application of deep learning and deep-RL for heavy-duty trucks in the field, which are significantly more complex than cars, is still sparse, however. In this article, we develop a two-layer gray-box deep learning model to capture longitudinal dynamics of heavy-duty trucks while abstracting their complexity and present an approach to properly break the nested feedback loops in the model for training. We compare this model with three other alternative models and show that it achieves $~10x$ better general performance compared to a standard artificial neural network and results in $~4x$ and $~40x$ slower steady-state acceleration and speed error growth rates, respectively. We then present an architecture to utilize these deep learning models within the deep-RL framework and use it to develop baseline CACC controllers that can be zero-shot transferred to the field. To carry out the work, we present a setup of differently configured trucks along with their interface architecture and stochastic driving cycle generators for data collection. Numerical validation of the approach demonstrated stationary and bounded modeling error, and demonstrated transfer of CACC controllers with consistent overshoot bounds and a stable approximately-zero steady-state error. Validation from field experiments demonstrated similarly consistent results. Compared to a state-of-the-art benchmark, the deep-RL controller achieved lower speed and time-gap error variance but higher time-gap error offset.     

Design of discrete PID controllers for maximizing stability margins

The objective of this paper is to provide a discrete PID controller design procedure for maximizing stability margins. First, a new and complete characterization of the entire set of stabilizing discrete PID controllers for a given plant is presented. Then, based on this characterization, an efficient algorithm is developed for testing if, for a given plant, there exists a digital PID controller gain parameter space corresponding to closed-loop poles being inside the circle of radius $\rho$ centered at the origin. The developed algorithm is finally applied along with a bisection strategy to determine, for a specified small positive number $\epsilon$, a minimum value ${\rho }_{\epsilon }^{*}$ and the corresponding ${\rho }_{\epsilon }^{*}-\text{stabilizing}$ discrete PID controller set for achieving at least $1-{\rho }_{\epsilon }^{*}$ of stability margin. To illustrate the features of our new characterization of stabilizing digital PID controller sets and the effectiveness of the presented algorithms to the maximum stability-margin discrete PID controller design, two numerical examples are provided.     

NDT-6D for color registration in agri-robotic applications

Registration of point cloud data containing both depth and color information is critical for a variety of applications, including in-field robotic plant manipulation, crop growth modeling, and autonomous navigation. However, current state-of-the-art registration methods often fail in challenging agricultural field conditions due to factors such as occlusions, plant density, and variable illumination. To address these issues, we propose the NDT-6D registration method, which is a color-based variation of the Normal Distribution Transform (NDT) registration approach for point clouds. Our method computes correspondences between pointclouds using both geometric and color information and minimizes the distance between these correspondences using only the three-dimensional (3D) geometric dimensions. We evaluate the method using the GRAPES3D data set collected with a commercial-grade RGB-D sensor mounted on a mobile platform in a vineyard. Results show that registration methods that only rely on depth information fail to provide quality registration for the tested data set. The proposed color-based variation outperforms state-of-the-art methods with a root mean square error (RMSE) of 1.1–1.6 cm for NDT-6D compared with 1.1–2.3 cm for other color-information-based methods and 1.2–13.7 cm for noncolor-information-based methods. The proposed method is shown to be robust against noises using the TUM RGBD data set by artificially adding noise present in an outdoor scenario. The relative pose error (RPE) increased $~$14% for our method compared to an increase of $~$75% for the best-performing registration method. The obtained average accuracy suggests that the NDT-6D registration methods can be used for in-field precision agriculture applications, for example, crop detection, size-based maturity estimation, and growth modeling.     

Convergence on iterative learning control of Hilfer fractional impulsive evolution equations

In this paper, impulsive fractional differential equations with Hilfer fractional derivatives of order and type $0\le \nu \le 1$ is considered. Convergence analysis of $P$-type and $P{I}^{\mu }$-type open-loop iterative learning scheme is studied in the sense of $\lambda$-norm. Examples are provided to explain the theory developed.     

Fast vision‐based autonomous detection of moving cooperative target for unmanned aerial vehicle landing

We propose a fast and effective method, fast target detection (FTD), to detect the moving cooperative target for the unmanned aerial vehicle landing, and the target is composed of double circles and a cross. The purpose of our strategy is to land on the target. The FTD method needs to detect the target at the high and low heights. At the high height, the target appears completely and stably in the camera field. The FTD method can detect the circle and cross to rapidly reach the target center, named cross and circle–FTD (). To detect the cross, we propose a slope distance equation to obtain the distance between two slopes. The proposed slopes cluster method, based on the distance equation and K‐means, is used to determine the cross center. At the low height, the target appears incompletely and unstably. Therefore, FTD methods detect only the cross, named cross–FTD (). We extract the cross features ( CFs) based on line segments. Then, four CFs are combined based on graph theory. Experiments on our four datasets show that FTD has rapid speed and good performance. (Our method is implemented in C++ and is available at https://github.com/Li-Zhaoxi/UAV-Vision-Servo .) On the Mohamed Bin Zayed International Robotics Challenge datasets made we constructed, detects the target from a image approximately per pipeline with F‐measure and tracks target approximately per pipeline with F‐measure. detects centers from a image at approximately per image with F‐measure.     

Distributed dynamic matrix control with constrained optimization for collision and obstacle avoidance of simulated multiple quadcopters

A system of fast moving quadcopters has a high risk of collisions with neighboring quadcopters or obstacles. The objective of this work is to develop a control strategy for collision and obstacle avoidance of multiple quadcopters. In this paper, the problem of distributed dynamic matrix control (DMC) for collision avoidance among a team of multiple quadcopters attempting to reach consensus in the horizontal plane and yaw direction ( $x,y$, and $\psi$) is investigated. Violations of a predetermined safety radius generates output constraints on the DMC optimization function, which has not been dealt with in the literature. Different from past works, the proposed strategy can perform collision avoidance in the $x$, $y$, and $z$-directions. In addition, logarithmic barrier functions are implemented as input rate constraints on the control actions. Extensive simulation studies for a team of quadcopters illustrate promising results of the proposed control strategy and case variations. In addition, DMC parameter effects on the system performance are studied, and a successful study for obstacle avoidance is presented.     

A new possibility degree measure for interval‐valued q‐rung orthopair fuzzy sets in decision‐making

As a generalization of the interval‐valued intuitionistic fuzzy sets, a consciousness of interval‐valued $q$‐rung orthopair fuzzy sets (IV $q$‐ROFSs) is a robust and trustworthy tool to fulfill the imprecise information with an adaptation of the manageable parameter $q\ge 1$. However, the ranking of any interval‐valued numbers is very valuable for interval‐valued decision‐making problems. Possibility degree measure is a worthy tool to manage the degree of possibility of one object over the other. Driven by these requisite characteristics, it is fascinating to manifest the possibility degree of comparison between two IV $q$‐ROFSs, and an innovative method is then encouraged to rank the given numbers. Few properties are checked to explain their features and exhibited the advantages of it over the existing possibility measures with some counterintuitive examples. Later on, we consider the multiattribute group decision making (MAGDM) method and embellish it with numerical examples, to rank the alternatives. Several numerical examples are implemented to test the superiority of the stated MAGDM method and to confer its more manageable and adaptable nature.     

Deep neural networks to predict autonomous ground vehicle behavior on sloping terrain field

Conventional large agricultural machinery or implements are unsafe and unsuitable to operate on slopes $>6^{\circ }$ or 10%. Tractor rollovers are frequent on slopes, precluding farming on arable hills, uneven or highly sloped land. Therefore, a fleet of autonomous ground vehicles (AGV) is proposed to cultivate highly sloped land ($>6^{\circ }$). The fleet aims to expand agricultural land to the slopes and to strengths the human-robot collaboration in an unsafe sloped environment. However, the fleet's success largely depends on vehicle behavior models regarding traction, mobility, and energy consumption on varying slopes. The vehicle intelligent behavior models are essential and would solve multiple objectives ranging from simulations to path planning & navigation. Therefore, this study aimed to build a deep learning-based vehicle behavior models on sloping terrain. A standard drawbar test was performed on a single AGV operating on an actual sloped field at varying speeds and load conditions. The drawbar test quantified the AGV's behavior on slopes in metrics related to traction (traction efficiency), mobility (travel reduction), and energy consumption (power number). Deep learning-based models were developed from the experimental data to predict the AGV's behavior on slopes as a function of vehicle velocity, drawbar, and slope. A special model called the proposed model, which combined multiple deep neural networks with a mixture of Gaussians, was developed and trained with a hybrid training method. The proposed model consistently outperformed the other well-known machine learning models. This study explored the capabilities of machine learning algorithms to simulate the behavior of small-track vehicle or AGV on sloping terrain. The fleet aims to provide safer agriculture keeping human safety in focus, and the developed predictive vehicle behavior models would empower the fleet's operation on currently unsafe sloped terrain by assisting in vehicle path planning, route optimization, and decision making.     

Risk-sensitive large-population linear-quadratic-Gaussian Games with major and minor agents

This paper studies large-population dynamic games involving a linear-quadratic-Gaussian (LQG) system with an exponential cost functional. The parameter in the cost functional can describe an investor's risk attitude. In the game, there are a major agent and a population of $N$ minor agents where $N$ is very large. The agents in the games are coupled via both their individual stochastic dynamics and their individual cost functions. The mean field methodology yields a set of decentralized controls, which are shown to be an $ϵ$-Nash equilibrium for a finite $N$ population system where $ϵ=O\left(\frac{1}{\sqrt{N}}\right)$. Numerical results are established to illustrate the impact of the population's collective behaviors and the consistency of the mean field estimation.     

Actuator fault detection for masonry robot manipulator arm with the interval observer

The main construction method of building wall is artificial masonry, the main problem is that the process is associated with low construction efficiency and poor safety, workers are prone fall from high altitude. The research of automatic masonry robot has become an urgent need. The masonry mechanical arm system is the main executing part of the masonry robot, special attention should be paid to the robot fault. Therefore, it is necessary to establish a suitable model to detect the actuator faults of the manipulator system. In this paper, a dynamic model of manipulator fault is presented and a fault detection scheme of masonry robot manipulator arm is proposed based on the model. The model is simplified by analyzing the state parameters of each joint during robot masonry and the interval observer with more design freedom was designed based on the established mathematical model of actuator faults. In this paper, a joint method for solving S and L matrices is proposed, which avoids the limitation of the traditional method for solving L matrices by two-step. In the presence of external interference, $l_1$/$H_{\infty }$ performance are introduced into the generation process of residual interval, and the interval observer has better disturbance robustness and fault sensitivity. Simulation experiments verify that the scheme can effectively detect the actuator fault of the manipulator, and experiments are carried out on a 6-axis manipulator. The experimental results show that when actuator faults occur at joints 2 and 3, the residual rapidly exceeds the threshold range, which proves the effectiveness of the fault detection scheme designed in this paper.     

On the regional control problem of the bilinear wave equation

The present research deals with regional optimal control problem of the bilinear wave equation evolving on a spatial domain $\mathrm{\Omega }\subset {\mathrm{ℝ}}^n,n\ge 1$. Such an equation is excited by bounded controls that act on the velocity term. It addresses the tracking of a desired state all over the time interval $[0,T]$ only on a subregion $\omega$ of $\mathrm{\Omega }$ with minimum energy. Then, we prove that an optimal control exists and is characterized as a solution to an optimality system. Algorithm for the computation of such a control is given and successfully illustrated through simulations.     

Bifurcation and control of disease spreading networks model with two delays

In this paper, the dynamical behaviors are investigated for a complex network with two independent delays. Instead of taking time delays as bifurcation parameters, we choose probability $p$ and parameter $\mu$ as the control parameters to study their effects on local stability and Hopf bifurcation, respectively. Moreover, the conditions for generating Hopf bifurcation are given. Furthermore, we further discuss the effects of two time delays on the critical values of parameters $p$ and $\mu$. Finally, numerical simulations are used to illustrate the validity of the obtained results.     

A generalized χ 2 divergence for multisource information fusion and its application in fault diagnosis

Dempster–Shafer theory is invaluable for handing uncertain problems in multisource information fusion field. But how to fuse highly conflicting information remains a pending issue. To deal with the issue, we propose a novel reinforced belief ${\chi }^2$ divergence measure (named as $\mathrm{??}{\chi }^2$ divergence) to calculate the conflict degree between evidence. The proposed $\mathrm{??}{\chi }^2$ divergence comprehensively considers the effects of the single-element subset and the multielement subset. In addition, the $\mathrm{??}{\chi }^2$ divergence has been proved to be a bounded, nondegenerate, and symmetrical divergence measure. Then, we design a new $\mathrm{??}{\chi }^2$ divergence-based multisource information fusion method. This method combines information volume weights and supports degree weights to modify the evidence before fusion. Finally, an application for fault diagnosis is provided to show that the proposed method is superior to other existing methods.     

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

For decades, motorsport has been an incubator for innovations in the automotive sector and brought forth systems, like, disk brakes or rearview mirrors. Autonomous racing series such as Roborace, F1Tenth, or the Indy Autonomous Challenge (IAC) are envisioned as playing a similar role within the autonomous vehicle sector, serving as a proving ground for new technology at the limits of the autonomous systems capabilities. This paper outlines the software stack and approach of the TUM Autonomous Motorsport team for their participation in the IAC, which holds two competitions: A single-vehicle competition on the Indianapolis Motor Speedway and a passing competition at the Las Vegas Motor Speedway. Nine university teams used an identical vehicle platform: A modified Indy Lights chassis equipped with sensors, a computing platform, and actuators. All the teams developed different algorithms for object detection, localization, planning, prediction, and control of the race cars. The team from Technical University of Munich (TUM) placed first in Indianapolis and secured second place in Las Vegas. During the final of the passing competition, the TUM team reached speeds and accelerations close to the limit of the vehicle, peaking at around $270\text{km h}{}^{-1}$ and $28ms{}^{-2}$. This paper will present details of the vehicle hardware platform, the developed algorithms, and the workflow to test and enhance the software applied during the 2-year project. We derive deep insights into the autonomous vehicle's behavior at high speed and high acceleration by providing a detailed competition analysis. On the basis of this, we deduce a list of lessons learned and provide insights on promising areas of future work based on the real-world evaluation of the displayed concepts.     

Dynamic event-triggered distributed interval functional observers for interconnected systems

In this paper, we design dynamic event-triggered interval functional observers (FOs) for interconnected systems comprising $M$ $(M\ge 2)$ subsystems where each subsystem is subject to nonlinearities and output disturbances. Our design method consists of two main steps. First, we design decentralized dynamic event-triggered mechanisms (ETMs) which use only locally measured output information. We then consider the design of distributed interval FOs by using the newly proposed ETMs. Their existence conditions are established and formulated in terms of linear programming. We also derive a bound on the estimated error vector and show that this bound is the smallest. Thus, this ensures that the unknown linear functional state vector can be estimated within an upper and lower bound of its true value by the designed interval observers. Finally, we apply the obtained results to design dynamic event-triggered interval observers for linear functions of the state vectors of an $N$-machine power system.     

Controllability of fractional dynamical systems with distributed delays in control using ψ-Caputo fractional derivative

This research investigates the controllability of linear and non-linear fractional dynamical systems with distributed delays in control using the $\psi$-Caputo fractional derivative. For controllability of linear systems, the positive definiteness of Grammian matrix, which is characterized by Mittag–Leffler functions, is used to provide necessary and sufficient conditions. For the controllability of non-linear systems, the iterative technique with the completeness of $X$ is used to obtain sufficient conditions. Using the $\psi$-Caputo fractional derivative, this study is new since it investigates the ideas of controllability. A couple of numerical results are offered to explain the theoretical results.     

Convergence properties concerning Lebesgue-p norm of iterative learning control for a class of fractional differential systems

This paper investigates the monotonic convergence and speed comparison of first- and second-order proportional-α-order-integral-derivative-type ( ${\mathit{PI}}^{\alpha }D-$ type) iterative learning control (ILC) schemes for a linear time-invariant (LTI) system, which is governed by the fractional differential equation with order $\alpha \in \left(1,2\right)$. By introducing the Lebesgue-p ( $L^P$) norm and utilizing the property of the Mittag-Leffler function and the boundedness feature of the fractional integration operator, the sufficient condition for the monotonic convergence of the first-order updating law is strictly analyzed. Therewith, the sufficient condition of the second-order learning law is established using the same means as the first one. The obtained results objectively reveal the impact of the inherent attributes of system dynamics and the constructive mode of the ILC rule on convergence. Based on the sufficient condition of first/second-order updating law, the convergence speed of first- and second-order schemes is determined quantitatively. The quantitative result demonstrates that the convergence speed of second-order law can be faster than the first one when the learning gains and weighting coefficients are properly selected. Finally, the effectiveness of the proposed methods is illustrated by the numerical simulations.     

Performance improvements of a sweet pepper harvesting robot in protected cropping environments

Using robots to harvest sweet peppers in protected cropping environments has remained unsolved despite considerable effort by the research community over several decades. In this paper, we present the robotic harvester, Harvey, designed for sweet peppers in protected cropping environments that achieved a 76.5% success rate on 68 fruit (within a modified scenario) which improves upon our prior work which achieved 58% on 24 fruit and related sweet pepper harvesting work which achieved 33% on 39 fruit (for their best tool in a modified scenario). This improvement was primarily achieved through the introduction of a novel peduncle segmentation system using an efficient deep convolutional neural network, in conjunction with three‐dimensional postfiltering to detect the critical cutting location. We benchmark the peduncle segmentation against prior art demonstrating an improvement in performance with a $F_1$ score of 0.564 compared to 0.302. The robotic harvester uses a perception pipeline to detect a target sweet pepper and an appropriate grasp and cutting pose used to determine the trajectory of a multimodal harvesting tool to grasp the sweet pepper and cut it from the plant. A novel decoupling mechanism enables the gripping and cutting operations to be performed independently. We perform an in‐depth analysis of the full robotic harvesting system to highlight bottlenecks and failure points that future work could address.