Autonomous Machinery Research Articles

Sensor-Enhanced Smart Gripper Development for Automated Meat Processing.

Grasping and object manipulation have been considered key domains of Cyber-Physical Systems (CPS) since the beginning of automation, as they are the most common interactions between systems, or a system and its environment. As the demand for automation is spreading to increasingly complex fields of industry, smart tools with sensors and internal decision-making become necessities. CPS, such as robots and smart autonomous machinery, have been introduced in the meat industry in recent decades; however, the natural diversity of animals, potential anatomical disorders and soft, slippery animal tissues require the use of a wide range of sensors, software and intelligent tools. This paper presents the development of a smart robotic gripper for deployment in the meat industry. A comprehensive review of the available robotic grippers employed in the sector is presented along with the relevant recent research projects. Based on the identified needs, a new mechatronic design and early development process of the smart gripper is described. The integrated force sensing method based on strain measurement and magnetic encoders is described, including the adjacent laboratory and on-site tests. Furthermore, a combined slip detection system is presented, which relies on an optical flow-based image processing algorithm using the video feed of a built-in endoscopic camera. Basic user tests and application assessments are presented.

A Brief Overview of Technologies in Automated Agriculture: Shaping the Farms of Tomorrow

As global population continues to grow, there is an increasing need for innovative solutions to enhance agricultural productivity, efficiency, and sustainability. To meet increasing population demand, agricultural production must be doubled. The global population is projected to rise by almost two billion individuals within the next three decades. With global challenges such as population growth, climate change, and resource formation, automation in farming practices is one of the key driving forces behind this revolution. Robotics, coupled with artificial intelligence (AI) and advanced data analytics, are transformative solutions for precision farming and smart farming technologies. These technologies enable continuous and efficient farming operations and provide detailed monitoring at a plant-by-pant level, optimizing resource use and reducing the environmental footprint. Technological advancements have led to the development of various robotic systems, including agricultural grippers and autonomous machinery, which are integral to the automation of farming tasks, from sowing to harvesting. However, the adoption of such technologies is not without challenges. High initial investment costs, connectivity issues, and data security are some of the barriers that need to be addressed. The potential benefits of reduced operational costs, improved crop quality, and enhanced farm output make it a promising solution for the future of farming. In this article, we discuss the multifaceted role of robotics in modern agriculture by exploring both technological advancements and challenges to widespread adoption.

Laboratory Scale Multiphysics Ship Machinery Plant for Autonomous Machinery Research

Laboratory Scale Multiphysics Ship Machinery Plant for Autonomous Machinery Research

Perspective: Closing the Regulatory Gap: Addressing Challenges for Autonomous Agricultural Equipment in California

HighlightsOutdated safety regulations pose challenges for autonomous agricultural tractors.Cal/OSHA denied the petition to update regulations for autonomous tractors.The industry's experimental variance shows potential but lacks sufficient data.Recommendations include third-party safety testing and creating an advisory group.Abstract.As of August 2024, California’s agricultural tractor safety regulations, developed over half a century ago, are still focused on classic tractors with human operators. These regulations are problematic when applied to autonomous equipment. Since agricultural equipment has advanced, producers have faced challenges in complying with existing regulations for autonomous machinery in California A petition (No. 596) was submitted in December 2021 to the State of California Department of Industrial Relations and reviewed in March 2023. The petition requesting modification of the agricultural tractor's traditional regulation was recently submitted to the Occupational Safety and Health Standards Board (OSHSB). The OSHSB denied both petitions. This article discusses more details related to California agricultural tractor safety regulations, the petition to modify the traditional regulations, discussions on OSHSB meetings regarding the petition, and several suggestions to resolve the current issue.. Keywords: Agricultural equipment, Autonomous, Regulation, Safety, Tractor

Virtual Reality Interface Evaluation for Earthwork Teleoperation

Automation and robotics are destined to play a critical role in the Industry 4.0 revolution, as illustrated by the emergence of autonomous machinery in earthwork operations. Despite rapid progress, autonomous agents will always require human supervision to instruct their mission and to guaranty safety when unexpected problems arise. Traditional human supervision requires an operator to physically enter each machine at risk and manually take control. This approach is time-consuming and requires highly qualified personnel capable of operating various machines. This process can be hastened and simplified by means of teleoperated supervision, which itself requires the appropriate interface. In this paper we evaluate a virtual reality (VR)-based interface using hybrid interactions and an immersive digital-twin compared to a real-life control. We compare these interfaces through control tasks performed by expert and non-expert operators, analyzing time and precision, as well as user feedback. The preliminary results show that the VR interface brings equivalent and satisfactory performances for experts and improves the efficiency of apprentices. Therefore, not only does everyone performs well in the virtual environment, but also the training time can be shortened significantly as non-experts can perform similarly under the same conditions.

Hazard Analysis of an Autonomous Container Handling System – a Comparison of STPA and HAZOP Methods

Increasing automation is a major trend in container terminals. In automated container handling systems, safety has been previously ensured by segregating the automated machinery from other traffic and workers moving on foot. Currently, further increases in flexibility are sought by developing autonomous systems that are capable of mixed-traffic operations without the need for separate operating areas. This increases the complexity of the systems and introduces new safety hazards. In addition to traditional hazard analysis methods, new approaches are needed to address the emergent risks related to autonomous operations. This paper studies the applicability of the STPA (system-theoretic process analysis) method in hazard analysis of an autonomous machine system. To support the evaluation, we define evaluation categories for comparison of the analysis methods. We also compare STPA with an established method, HAZOP (hazard and operability study). To perform the comparison, both STPA and HAZOP are applied to an autonomous container handling system concept. The study suggests that both STPA and HAZOP are well suited to support the development of autonomous machinery. However, we also highlight some notable differences in the methods, mostly related to the different underlying accident models that they utilise. HAZOP is an established method with tools and standards available. STPA, on the other hand, provides a well-defined syntax to ensure the analysis quality and a system modelling approach that supports the system development.

Autonomous Machinery Management for Supervisory Risk Control Using Particle Swarm Optimization

Safe navigation for maritime autonomous surface ships (MASS) is a challenging task, and generally highly dependent on effective collaboration between multiple sub-systems in environments with various levels of uncertainty. This paper presents a novel methodology combining risk-based optimal control and path following with autonomous machinery management (AMM) for MASS navigation and supervisory risk control. Specifically, a risk-aware particle swarm optimization (PSO) scheme utilizes “time-to-grounding” predictions based on weather data and electronic navigational charts (ENC) to simultaneously control both the ship’s motion as well as the machinery system operation (MSO) mode during transit. The proposed autonomous navigation system (ANS) is comprised of an online receding horizon control that uses a PSO approach from previous works, which produces a dynamic risk-aware path with respect to grounding obstacles from a pre-planned MASS path, subsequently given as the input to a line-of-sight guidance controller for path following. Moreover, the MSO mode of the AMM system is simultaneously selected and assigned to explicit segments along the risk-aware path throughout the receding horizon, which effectively introduces into the optimization scheme an additional safety layer as well as another dimension for risk or resource minimization. The performance of the resulting ANS is demonstrated and verified through simulations of a challenging scenario and human assessment of the generated paths. The results show that the optimized paths are more efficient and in line with how human navigators would maneuver a ship close to nearby grounding obstacles, compared to the optimized paths of selected previous works.

Rotation Control, Interlocking, and Self‐Positioning of Active Cogwheels

Gears and cogwheels are elemental components of machines. They restrain degrees of freedom and channel power into a specified motion. Building and powering small‐scale cogwheels are key steps toward feasible micro and nanomachinery. Assembly, energy injection, and control are, however, a challenge at the microscale. In contrast with passive gears, whose function is to transmit torques from one to another, interlocking and untethered active gears have the potential to unveil dynamics and functions untapped by externally driven mechanisms. Here, it is shown the assembly and control of a family of self‐spinning cogwheels with varying teeth numbers and study the interlocking of multiple cogwheels. The teeth are formed by colloidal microswimmers that power the structure. The cogwheels are autonomous and active, showing persistent rotation. Leveraging the angular momentum of optical vortices, we control the direction of rotation of the cogwheels. The pairs of interlocking and active cogwheels that roll over each other in a random walk and have curvature‐dependent mobility are studied. This behavior is leveraged to self‐position parts and program microbots, demonstrating the ability to pick up, direct, and release a load. The work constitutes a step toward autonomous machinery with external control as well as (re)programmable microbots and matter.

The first International Conference on Mechanical System Dynamics grandly convened in Nanjing, China

The first International Conference on Mechanical System Dynamics grandly convened in Nanjing, China

Efficiency-Oriented MPC Algorithm for Path Tracking in Autonomous Agricultural Machinery

Path-tracking control algorithms in agriculture typically focus on how to improve the trajectory-tracking performance of autonomous agricultural machinery, and the agricultural productivity is optimized in a two-layer way. The upper operational layer optimizes an optimal tracking trajectory with the best agricultural productivity, and the lower control layer—such as Nonlinear Model Predictive Control (NMPC)—receives this optimized tracking trajectory first, and then steers the vehicle to track this trajectory with high accuracy. However, this two-layer structure cannot improve the agricultural productivity at the control layer online, which makes the agricultural operation sub-optimal. In this paper, we focus on agricultural machinery operational efficiency, to represent agricultural productivity; in order to realize optimizing control to further improve agricultural machinery operational efficiency, a new path-tracking control algorithm, named Efficiency-oriented Model Predictive Control (EfiMPC), is proposed. EfiMPC is intrinsically a nested structure, which can consider the global performance of the whole system defined in the operational layer—like the agricultural machinery operational efficiency considered in this paper—in the control layer online; thus, the agricultural machinery operational efficiency can be improved during the farming operation. An unreachable tracking point, denoted as the pseudo-point, has been proposed, to indicate the agricultural machinery operational efficiency objective in a receding horizon fashion; EfiMPC can utilize this pseudo-point to realize the optimizing control online. A simulation case study was used to test the superiority of the proposed EfiMPC algorithm, and the results show that, compared with the traditional NMPC algorithm, the agricultural machinery operational efficiency realized by EfiMPC was improved by 8.56%; thus, the effectiveness of the EfiMPC has been demonstrated.

Improving productivity in block/panel caving through dynamic confinement of semi-autonomous load-haul-dump machines

ABSTRACT The incorporation of autonomous equipment is fuelling a revolution in mining, but it poses operational challenges due to potential hazardous interactions between humans and the autonomous machinery. For this reason, block and panel caving mines operate under a static confinement policy that stops all autonomous LHDs when workers must enter the production area, thus having a huge negative impact on productivity. To address this challenge, we propose two dynamic confinement policies, and we model them using discrete event simulation. Results show that the dynamic moderate/intensive policies may lead to increases in production of 35%/45% when compared with the static policy.

Toward Autonomous Vehicles and Machinery in Mill Yards of the Forest Industry: Technologies and Proposals for Autonomous Vehicle Operations

The use of autonomous systems on the wood processing sites of forest industries can significantly increase safety, productivity, and efficiency by reducing the number of monotonous and dangerous tasks conducted by human labor utilizing heavy machinery. However, autonomous machines are challenging and difficult to implement in the mill yards environment because of the dynamic and complex working environment, and partly unstructured processes. The inherent complexity of wood handling and storage tasks is requiring significant human expertise. Fast advancements in sensor technologies, machine learning techniques, and increases in the computational power enable progress in automated operation framework and algorithms development. This opens the door to introduce novel autonomous systems into this environment. With the aim of gaining a better understanding of current issues and facilitating optimal strategies for deploying high-level autonomous systems for the mill yard environments, this study: (1) Utilized a systematic literature review to map current autonomous technologies and algorithms suitable for adoption by the forest industry in automation of vehicles working in mill yards; (2) Summarizes and discusses the potential feasibility of the considered sensors and systems, adaption strategy, and high-level autonomous machinery implementation challenges in the mill yard environment; and (3) Proposes a system framework that integrates multiple technologies to enable autonomous navigation and material handling in mill yards. The study is the first of its kind as a comprehensive study on autonomous vehicles and machinery in mill yard environments. Our novel framework aids in the identification of follow-up research areas and thus promotes the adoption and use of complex autonomous systems in industrial environments.

How will regulation influence commercial viability of autonomous equipment in US production agriculture?

AbstractAutonomous equipment for crop production is on the brink of commercialization in the United States but federal, state, and local policies could affect commercial viability and hinder adoption. This article examines the farm‐level implications of both a speed restriction and on‐site supervisory regulations. The rules reduce the profitability of autonomous machinery, and for some scenarios autonomous machines are no longer an economically viable alternative to conventional machinery. Regulations also increase the optimal number autonomous machines required and influence production practices. Smaller farms have more flexibility in supporting the rules because they have more to gain from the use of autonomous equipment.

Automation and Robotics in Forest Harvesting Operations

Technology development, in terms of both capability and cost-effective integration, is moving at a fast pace. While advanced robotic systems are already commonplace in controlled workspaces such as factories, the use of remote controlled or autonomous machines in more complex environments, such as for forest operations, is in its infancy. There is little doubt autonomous machinery will play an important role in forest operations in the future. Many machine functions already have the support of automation, and the implementation of remote control of the machine where an operator can operate a piece of equipment, typically in clear line-of sight, at least is commonly available. Teleoperation is where the operator works from a virtual environment with live video and audio feedback from the machine. Since teleoperation provides a similar operator experience to working in the machine, it is relatively easy for an operator to use teleoperation. Autonomous systems are defined by being able to perform certain functions without direct control of a human operator. This paper presents opportunities for remote control, teleoperated machines in forest operations and presents examples of existing developments and ideas from both forestry and other industries. It identified the extraction phase of harvesting as the most logical placement of autonomous machines in the near-term. The authors recognise that, as with all emerging technologies and sectors, there is ample scope for differences in opinions as to what will be commercially successful in the future.

An overview of current safety requirements for autonomous machines – review of standards

Abstract The development of automated work machine systems towards autonomous operation is proceeding rapidly in different industrial sectors. The aim of the study was to explore the current situation and possible differences in standardization supporting the development in different industrial sectors. The existing ISO and IEC standards and work items related to autonomous machinery were reviewed as well as activities in international industry groups regarding automation and autonomy of machinery. In general, the current standards are made mostly for machine manufacturers, but the views or responsibilities at worksite level are not considered. Three different approaches for safety concepts for different operating conditions were identified. One of them relies on onboard safety systems including sensor and perception systems for indoor applications. One guides to separate and isolate the autonomously operating machinery and to use access control to the autonomous operating zone. The third one is relying mainly on the machine operator’s ability to understand the situation and to react correctly according to the available information. From technology point of view there seems to be a gap between the safety requirements set in standards and the state of the art in currently available technology.

Managing soil compaction – A choice of low-mass autonomous vehicles or controlled traffic?

Compaction-induced soil degradation is of growing importance as field machinery continues to increase in power and mass. Approaches to managing the impacts of soil compaction include minimisation (reduce load), remediation (tillage) and confinement (control traffic). Integrated ‘swarms’ of low-mass autonomous machinery have recently been suggested as a means of reducing compaction and an alternative to controlled traffic. In this study, combine and potato harvester machinery relationships were used to predict the specifications of potential low-mass harvesters for use in soil compaction modelling. Results suggested that combine harvester gross vehicle mass (GVM) must be less than 6 Mg to keep the modelled soil bulk density below 1.4 Mg m−3. With this constraint, 6–9 small harvesters (~50 kW) would be required to replace one Class 9 (>300 kW) harvester. A fleet of this size would require access to unloading facilities every 2.5–3 min for the modelled yield conditions. For root and tuber harvesting, which results in a high degree of soil disturbance, no low-mass harvester option was found that would avoid compacting the soil to unacceptable limits. Avoiding soil compaction while maintaining acceptable productivity will pose considerable design and logistics challenges for low-mass grain, root and tuber vegetable harvest machinery. The integration of controlled traffic farming (CTF) and medium-capacity autonomous machines (~10–20 Mg GVM for combine harvesters) may be a better solution for both soil compaction and operational logistics than low-mass swarm technology.

Path planning of autonomous agricultural machineries in complex rural road

This study mainly investigates the path planning and collision avoidance problem of autonomous agricultural machineries in the complex rural road. First, the grid model is used to build the global map of machineries and the particle swarm optimisation algorithm is used to search the global path. A non-linear dynamic inertia weight is introduced to avoid the local lock-up problem of traditional particle swarm optimisation. Then the artificial potential energy method for addressing the local collision problem is used, which is the sum of the attraction of the target point and the repulsion of the obstacles and the road boundary force. To solve the problem of the slip and roll of autonomous machinery caused by complex roads, a machinery dynamic model considering road curvature and topographic inclination is established, and the model predictive control algorithm is used to track the planned path. The simulation results show that proposed method can make the autonomous agricultural machinery driving safely and smoothly.

An economic feasibility assessment of autonomous field machinery in grain crop production

A multi-faceted whole farm planning model is developed to compare conventional and autonomous machinery for grain crop production under various benefit, farm size, suitable field day risk aversion, and grain price scenarios. Results suggest that autonomous machinery can be an economically viable alternative to conventional manned machinery if the establishment of intelligent controls is cost effective. An increase in net returns of 24% over operating with conventional machinery is found when including both input savings and a yield increase due to reduced compaction. This study also identifies the break-even investment price for intelligent controls for the safe and reliable commercialization of autonomous machinery. Results indicate that the break-even investment price is highly variable depending on the financial benefits resulting from the deployment of autonomous machinery, farm size, suitable field day risk aversion, and grain prices. The maximum break-even investment price for intelligent, autonomous controls is nearly US$500 000 for the median days suitable for fieldwork when including both input savings and a yield increase due to reduced compaction.

Projective simulation with generalization

The ability to generalize is an important feature of any intelligent agent. Not only because it may allow the agent to cope with large amounts of data, but also because in some environments, an agent with no generalization capabilities cannot learn. In this work we outline several criteria for generalization, and present a dynamic and autonomous machinery that enables projective simulation agents to meaningfully generalize. Projective simulation, a novel, physical approach to artificial intelligence, was recently shown to perform well in standard reinforcement learning problems, with applications in advanced robotics as well as quantum experiments. Both the basic projective simulation model and the presented generalization machinery are based on very simple principles. This allows us to provide a full analytical analysis of the agent’s performance and to illustrate the benefit the agent gains by generalizing. Specifically, we show that already in basic (but extreme) environments, learning without generalization may be impossible, and demonstrate how the presented generalization machinery enables the projective simulation agent to learn.

Crop Production of the future – possible with a new approach?

A view into future requires maybe a new approach of current arable production systems. So far crop production systems have been adapted to the machinery available on the market. New technological progresses in the range of precision and digital farming show the possibilities meeting the requirements of plants even more small-spatial and efficiently. Especially small autonomous machinery may offer opportunities achieving sustainable intensification in crop farming. Different crop productions and cultivation systems have to be devised, analyzed and assessed in this context.