Autonomous Ground Research Articles

Adaptive Synthesized Fault-Tolerant Autonomous Ground Vehicle Control With Guaranteed Performance and Saturated Input

Adaptive Synthesized Fault-Tolerant Autonomous Ground Vehicle Control With Guaranteed Performance and Saturated Input

Expanding Ground Vehicle Autonomy into Unstructured, Off-Road Environments: Dataset Challenges

As with the broad field of deep learning, autonomy is a research topic that has experienced a heavy explosion in attention from both the scientific and commercial industries due to its potential for the advancement of humanity in many cross-cutting disciplines. Recent advancements in computer vision-based autonomy has highlighted the potential for the realization of increasingly sophisticated autonomous ground vehicles for both commercial and non-traditional applications, such as grocery delivery. Part of the success of these technologies has been a boon in the abundance of training data that is available for training the autonomous behaviors associated with their autonomy software. These data abundance advantage is quickly diminished when an application moves from structured environments, i.e., well-defined city road networks, highways, street signage, etc., into unstructured environments, i.e., cross-country, off-road, non-traditional terrains. Herein, we aim to present insights, from a dataset perspective, into how the scientific community can begin to expand autonomy into unstructured environments, while highlighting some of the key challenges that are presented with such a dynamic and ever-changing environment. Finally, a foundation is laid for the creation of a robust off-road dataset being developed by the Engineer Research and Development Center and Mississippi State University’s Center for Advanced Vehicular Systems.

Safe and Reliable Movement of Fast LiDAR-based Self-driving Vehicle

Classification of objects is an important technique for autonomous ground vehicles to identify a surrounding environment and execute safe path planning. In this paper, a method based on horizontal segmentation is proposed to detect cone-shaped objects in vehicle’s vicinity using a LiDAR sensor. A captured point cloud is divided into five layers based on height information, and the division of detected objects into two groups, cones and others, has been made using classifiers available in MATLAB toolboxes. To separate the classified conical objects into four types used to mark the route, an algorithm for their recognition was developed and used. The proposed solution, verified by navigation experiments in real conditions using an unmanned racing car, has gave good results, i.e., a high rate of cone-shaped objects classification, a short processing time and a low computational load. The performed tests have allowed also to diagnose the causes of incorrect classification of objects. Thus, the experimental results indicated that the approach presented in this work can be used in real time for autonomous, collision-free driving along marked routes.

Hierarchical Piecewise-Trajectory Planning Framework for Autonomous Ground Vehicles Considering Motion Limitation and Energy Consumption

Hierarchical Piecewise-Trajectory Planning Framework for Autonomous Ground Vehicles Considering Motion Limitation and Energy Consumption

Design and application of finite‐time tracking control for autonomous ground vehicle affected by external disturbances

Design and application of finite‐time tracking control for autonomous ground vehicle affected by external disturbances

Security Analysis for Autonomous Ground Vehicle Under Stealthy Sensor Attacks

Security Analysis for Autonomous Ground Vehicle Under Stealthy Sensor Attacks

Trajectory Planning Design for Parallel Parking of Autonomous Ground Vehicles with Improved Safe Travel Corridor

In this paper, the concept of symmetry is utilized to design the trajectory planning for parallel parking of autonomous ground vehicles—that is, the construction and the solution of the optimization-based trajectory planning approach are symmetrical. Parking is the main factor that troubles most drivers for their daily driving travel, and it can even lead to traffic congestion in severe cases. With the rise of new intelligent and autonomous vehicles, automatic parking seems to have become a trend. Traditional geometric planning methods are less adaptable to parking scenarios, while the parking paths planned by graph search methods may only achieve local optimality. Additionally, significant computational time is often required by numerical optimization methods to find a parking path when a good initial solution is not available. This paper presents a hierarchical trajectory planning approach for high-quality parallel parking of autonomous ground vehicles. The approach begins with a graph search layer to roughly generate an initial solution, which is refined by a numerical optimization layer to produce a high-quality parallel parking trajectory. Considering the high dimensionality and difficulty of finding an optimal solution for the path planning optimization problem, this paper proposes an improved safe travel corridor (I-STC) with the construction of collision constraints isolated from surrounding environmental obstacles. By constructing collision constraints of the I-STC based on the initial solution, the proposed method avoids the complexities and non-differentiability of traditional obstacle avoidance constraints, and simplifies the problem modeling the subsequent numerical optimization process. The simulation results demonstrate that the I-STC is capable of generating parallel parking trajectories with both comfort and safety.

Evaluation of metaheuristic optimization algorithms applied to path planning

The development of autonomous ground vehicles is a topic widely discussed by the international scientific community. Path planning for these robots is an essential element to achieving a satisfactory fulfillment of missions without human intervention. This work adopts and improves the metaheuristic optimization methods harmony search and ant colony optimization, developing strategies based on the enhanced harmony search and improved ant colony optimization to achieve short, collision-free paths for robot movement. A description of the original methods and improvement procedures is presented, and their performance in path planning is compared through simulation. Theoretical results are supported by simulated experiments, which highlight the situations in which each metaheuristic algorithm is preferable and the benefits they bring to the path planning strategy.

Energy-Aware 3D Path Planning by Autonomous Ground Vehicle in Wireless Sensor Networks

Wireless sensor networks are used to monitor the environment, to detect anomalies or any other problems and risks in the system. If used in the transportation network, they can monitor traffic and detect traffic risks. In wireless sensor networks, energy constraints must be handled to enable continuous environmental monitoring and surveillance data gathering and communication. Energy-aware path planning of autonomous ground vehicle charging for sensor nodes can solve energy and battery replacement problems. This paper uses the Nearest Neighbour algorithm for the energy-aware path planning problem with an autonomous ground vehicle. Path planning simulations show that the Nearest Neighbour algorithm converges faster and produces a better solution than the genetic algorithm. We offer robust and energy-efficient path planning algorithms to swiftly collect sensor data with less energy, allowing the monitoring system to respond faster to anomalies. Positioning communicating sensors closer minimizes their energy usage and improves the network lifetime. This study also considers the scenario in which it is recommended to avoid taking direct travelling pathways between particular node pairs for a variety of different reasons. To address this more challenging scenario, we provide an Obstacle-Avoided Nearest Neighbour-based approach that has been adapted from the Nearest Neighbour approach. Within the context of this technique, the direct paths that connect the nodes are restricted. Even in this case, the Obstacle-Avoided Nearest Neighbour-based approach achieves almost the same performance as the the Neighbour-based approach.

Abnormal Driving Area Detection Using Multiple Vehicle Dynamic Model-Based Filter: Design and Experimental Validation

The main concern of remote control systems for autonomous ground vehicles (AGVs) is to perform the given mission according to the purpose of the operator. Although most remote systems are composed of a screen-based architecture, they are insufficient to transfer sufficient information to the remote operator. Therefore, in this paper, we present and experimentally validate an abnormal driving area detection system using an interacting multiple model (IMM) filter for the remote control system. In the proposed IMM filter, the unknown dynamic behavior of the vehicle, which changes according to changes in the driving environment, was lumped into a parameter change of the system model. As a result, we can obtain the probability of each model representing the reliability of each model, but an index can be used to infer the current status of the AGV and the driving environment. The index can help us detect both unusual behavior of the AGV such as skidding or sliding, and areas with low-friction road conditions that are not confirmed by images from the camera sensor. Thus, the remote operator can directly decide whether to continue operating or not. The proposed method is simple but useful and meaningful for the remote operator compared to the image-only method. The overall procedure of the proposed method was experimentally validated via a multi-purpose AGV on rough unpaved proving ground. Nine abnormal driving areas were discovered on the ground. In five of these areas, vehicles consistently exhibited abnormal driving behavior. The remaining four areas were confirmed to be affected by variables such as weather conditions and vehicle tire wear.

Autonomous ground vehicle gravity anomaly measurement and dynamic error compensation

Abstract To address the issue that dynamic gravity anomaly measurement is overly dependent on GNSS and can’t be measured autonomously at this stage, this paper proposes an autonomous ground vehicle dynamic gravity anomaly measurement method based on a strapdown inertial navigation system (SINS), odometer (OD), barometer and platform gravimeter. The SINS/OD /barometer integrated navigation solution delivers high-precision navigation parameters, completes the calculation of correction terms, and performs the autonomous dynamic gravity anomaly measurement combined with the primary measurement results of the platform gravimeter. Numerical calculations provide the requirements for the application of the proposed method, and the cut-off frequency for extracting gravity anomalies is 0.02 Hz, as determined by power spectral density analysis. In order to further improve the measurement accuracy and account for dynamic errors caused by vehicle maneuvering, a long-short-term memory (LSTM) model of recurrent neural network (RNN) is introduced. A series of experiments under multiple circumstances with repeated lines were conducted in Tianjin, China, and the static measurements along the line were taken using CG-5 to provide true values of gravity anomalies. The results demonstrate that the autonomous measurement scheme can achieve accuracy comparable to GNSS-assisted, and that dynamic error compensation algorithm based on LSTM improves the dynamic gravity measurements accuracy significantly without sacrificing the spatial resolution of gravity anomalies.

Effects of the Human Presence among Robots in the ARIAC 2023 Industrial Automation Competition

ARIAC is a robotic simulation competition promoted by NIST annually since 2017, aiming to present competitors’ with contemporary industry problems to be solved using agile robotics. For the 2023 competition, ARIAC competitors must perform assembly and kitting tasks by controlling four autonomous ground vehicles (AGVs), one floor-based robot, and one ceiling-based (Gantry) robot in an attempt to overcome a range of agility challenges in the supplied simulated environment, itself based on the Robot Operating System (ROS 2) and Gazebo. The 2023 competition also included a “human” agility challenge, comprising a (simulated) human operator working among robots on the factory floor. This development was motivated by the fact that, while robots and automation play an increasingly significant role in modern manufacturing, there still remains a close relationship between machines and humans. They should complement each other’s strengths and cover each other’s limitations while also observing any required safety rules. For example, the ISO standard “Robots and Robotic Devices – Collaborative robots” (ISO 15066:2016) prescribes the distances required between humans and robots. Within the ARIAC simulation environment, each human operator is controlled using autonomous Belief-Desire-Intention (BDI) agents. At the same time, competitors can monitor the position of each human operator at any time by subscribing to the relevant ROS topic. In this article, we analyse the effects of this (simulated) human presence in the 2023 ARIAC competition and perform a detailed analysis of how the three different human personalities that were implemented affect the assembly tasks undertaken at the four different locations of the assembly stations. Given how the system is currently implemented, it appears that the influence of each encoded personality on the competitors is not as predictable as anticipated. We expand on why this may be a problem when addressing real collaborative spaces involving humans and industrial robots and the improvements that can be undertaken to mitigate the ensuing problems.

Neuro-adaptive path following control of autonomous ground vehicles with input deadzone

This article investigates the path-following control problem of an autonomous ground vehicle (AGV) with unknown external disturbances and input deadzones. Neural networks are used to estimate unknown external disturbances, dead zones, and nonlinear functions. The minimum learning parameter scheme is employed to adjust the neural network to reduce the computational load. A backstepping control is proposed to facilitate the tracking of the target path. The steady-state path-following error is decreased by adding an integral error term to the backstepping controller. Command filtering is employed to address the explosion of the complexity issue of the conventional backstepping approach, and the filtering error is compensated via an auxiliary signal. Lyapunov stability study indicates that the AGV closed-loop system is bounded by the proposed control with reasonable accuracy. At last, simulations are given to demonstrate the potential of the proposed scheme in path-following control.

Continuous Online Semantic Implicit Representation for Autonomous Ground Robot Navigation in Unstructured Environments

Continuous Online Semantic Implicit Representation for Autonomous Ground Robot Navigation in Unstructured Environments

Small Autonomous Vehicles in Pedestrian Contexts: A First Analysis of Logistics Performances in Terms of Commercial Speed

There is a lot of talk about autonomous vehicles, and Europe is very much focusing on their use and deployment. However, the field use of these vehicles is still very limited. The proposed research refers to a specific category of autonomous vehicles, that is, small ground autonomous vehicles circulating in pedestrian environments, with a focus on their use in operating logistical services. More precisely, this paper presents data collected during a challenging experiment carried out in the city of Trikala, Greece, in the context of the major European project “SHOW”. A statistical analysis of these sampled data concerning service times, in terms of commercial speed, for collecting organic waste from cafeterias is presented. The aim of this paper is to verify whether data collected from autonomous vehicles used for this service are reliable and whether accurate estimates can be derived from these data to be used as standard parameters of these vehicles. For these reasons, we analyze the operational performance of the service performed by small autonomous vehicles, with particular attention to the interactions between them and pedestrians and the ability of users to load and unload small vehicles. More precisely, we verify whether there is an adaptation period in which human–vehicle interactions become smoother and whether commercial speed varies at different times of day, that is, if there are peak periods in which droid speed is limited because of the intensity of interactions with pedestrians. A statistical analysis of these data is proposed to find answers to these research questions. It made it possible to highlight an adaptation curve of humans to droids and that no peak periods emerged where droid speed was limited because of the intensity of interactions with pedestrians. This result is probably related to the fact that stability of service operation was not achieved. Had the period of experimentation been extended, it would probably have been possible to identify peak and off-peak periods and the relative commercial speeds. However, it is important to note that the achievement of service operation stability takes a long time. The results obtained are interesting and contribute to the current state of knowledge. In fact, data analyzed here are collected on public land, refer to interactions that take place between small autonomous ground vehicles and a heterogeneous population, and therefore constitute a starting point for the development of technologies that facilitate human–driver interactions and thus lead to an improvement in the performance of sustainable logistics services managed by autonomous vehicles and facilitate their dissemination.

Geometric Fidelity Requirements for Meshes in Automotive Lidar Simulation

The perception of vegetation is a critical aspect of off-road autonomous navigation, and consequentially a critical aspect of the simulation of autonomous ground vehicles (AGVs). Representing vegetation with triangular meshes requires detailed geometric modeling that captures the intricacies of small branches and leaves. In this work, we propose to answer the question, “What degree of geometric fidelity is required to realistically simulate lidar in AGV simulations?” To answer this question, in this work we present an analysis that determines the required geometric fidelity of digital scenes and assets used in the simulation of AGVs. Focusing on vegetation, we use a comparison of the real and simulated perceived distribution of leaf orientation angles in lidar point clouds to determine the number of triangles required to reliably reproduce realistic results. By comparing real lidar scans of vegetation to simulated lidar scans of vegetation with a variety of geometric fidelities, we find that digital tree models (meshes) need to have a minimum triangle density of >1600 triangles per cubic meter in order to accurately reproduce the geometric properties of lidar scans of real vegetation, with a recommended triangle density of 11,000 triangles per cubic meter for best performance. Furthermore, by comparing these experiments to past work investigating the same question for cameras, we develop a general “rule-of-thumb” for vegetation mesh fidelity in AGV sensor simulation.

Optimizing energy expenditure in agricultural autonomous ground vehicles through a GPU-accelerated particle swarm optimization-artificial neural network framework

The accurate energy consumption prediction in Agricultural Ground Vehicles (AGVs) holds immense potential for optimizing operational efficiency and minimizing environmental impact. However, existing optimization methods for such prediction tasks often suffer from high computational demands, hindering their practical implementation. This paper introduces a ground-breaking approach that overcomes this limitation by leveraging the potent computational power of Graphics Processing Units (GPUs) to accelerate the optimization process dramatically. We propose a novel adaptation of the Particle Swarm Optimization (PSO) algorithm, specifically tailored to the intricate multi-objective challenges of AGV energy prediction. This framework harnesses the strengths of a multi-objective approach, enabling the simultaneous optimization of prediction accuracy and model complexity. To further enhance efficiency, we seamlessly integrate GPU parallelization techniques, significantly expediting both the optimization process and the training of Artificial Neural Networks (ANNs) employed for prediction. Preliminary results demonstrate a remarkable improvement in the accuracy of AGV energy consumption predictions, directly attributed to the synergistic effect of optimizing the ANN architecture and parameters through our proposed PSO framework. This tailored PSO adaptation distinguishes itself by its ability to tackle the complex multi-objective nature of AGV energy prediction with enhanced efficiency and precision. It thus emerges as a compelling and novel solution within the realm of Machine Learning and heuristic methods for agricultural robotics, paving the way for sustainable and optimal AGV operations.

Override forces through clumps of small vegetation

Vegetation override is an important aspect of off-road ground vehicle mobility. For autonomous ground vehicles (AGV), path-planning in off-road environments may be informed by the predicted resistance of vegetation in the navigation environment. However, there are no prior measurements on the override resistance of small stems (<2.5 cm) and groups of small stems on medium-sized (≈1000 kg) vehicles. In this work, a series of override measurements for clumps of small vegetation that are relevant for off-road navigation by intermediate-sized AGV is presented. The development and calibration of a custom-made pushbar system with integrated load cells for directly measuring override forces is also presented, and a comparison of the results of the experiments to models developed for override of larger single stems is made. It is found that for clumps of small vegetation, the total override force is best predicted by the diameter of the largest stem in the clump. Additionally, it is found that the equations developed for larger stems under-predict the override forces exerted by smaller stems by about a factor of two.

An improved method planning path of an autonomous ground robot with using the MBD-RRT*FFT algorithm

The study analyzed the problems associated with planning the path of robots and increasing the accuracy and reliability of their guidance in real time. To solve this problem, a practical study was conducted, based on the obtained analysis, a modification of the asymptotically optimal BD-RRT*FT algorithm and was proposed and improved the MBD-RRT*FFT algorithm. was developed. Which, when applied in dynamic environments due to the use of multi-threaded computations, has better dynamic scheduling capabilities, and the use of Fitch's optimal search result selection algorithm provides a general tendency to choose the optimal straight-line path. To verify the effectiveness of the proposed algorithm improvement method, simulations were carried out using our own simulation program. A comparison of the MBD-RRT*FFT algorithm with other algorithms was carried out on three maps with an evaluation of performance indicators. In order to evaluate the behavior of the program, as well as to identify bottlenecks in the execution of the algorithm, the memory load of the decision-making system was monitored at all stages of the verification. This made it possible to see an increase in the consumption of memory resources by 12-15%, but taking into account the obtained results regarding the optimality and speed of calculations, conclusions were made regarding the minimal impact. Additional understanding of the need for careful selection of the hardware component of decision-making systems is provided. The scientific novelty of the method consists in the application of a scientific and methodological apparatus for improving the BD-RRT*FT algorithm, which ensured further development in terms of expanding and supplementing known data on algorithms with robot path planning and increasing accuracy and reliability in real time. The method includes methods of research, systematization and adjustment of new and previously acquired knowledge.

Security Control of Autonomous Ground Vehicles Under DoS Attacks via a Novel Controller With the Switching Mechanism

Security Control of Autonomous Ground Vehicles Under DoS Attacks via a Novel Controller With the Switching Mechanism