A comprehensive survey of unmanned ground vehicle terrain traversability for unstructured environments and sensor technology insights

This article provides a detailed analysis of the assessment of unmanned ground vehicle terrain traversability. The analysis is categorized into terrain classification, terrain mapping, and cost-based traversability, with subcategories of appearance-based, geometry-based, and mixed-based methods. The article also explores the use of machine learning (ML), deep learning (DL) and reinforcement learning (RL) and other based end-to-end methods as crucial components for advanced terrain traversability analysis. The investigation indicates that a mixed approach, incorporating both exteroceptive and proprioceptive sensors, is more effective, optimized, and reliable for traversability analysis. Additionally, the article discusses the vehicle platforms and sensor technologies used in traversability analysis, making it a valuable resource for researchers in the field. Overall, this paper contributes significantly to the current understanding of traversability analysis in unstructured environments and provides insights for future sensor-based research on advanced traversability analysis

Towards Semi-Autonomous Control of Heavy-Duty Tracked Earth-Moving Mobile Manipulators : Use Case: The Bulldozer

A mobile manipulator (MM) comprises a manipulator attached to a mobile base, making it capable of manipulation tasks in large workspaces. In the field of construction, heavy-duty MMs are extensively used for soil excavation at construction sites. One such machine is the bulldozer, which is widely used because of its robustness and maneuverability. With its onboard blade, the bulldozer shapes terrain and transports soil material by pushing it. However, operating the blade with joysticks to accurately shape the terrain surface and moving material productively are difficult tasks that require extensive training and experience. Automating the motion of the blade, therefore, has the potential to reduce skill requirements, improve productivity, and reduce operators’ workloads. This thesis studies and develops methods for the semi-autonomous control of a bulldozer to increase surface quality and earthmoving productivity. These goals were reflected in the main research problems (RPs). Furthermore, as bulldozers drive over the terrain shape generated by the blade, the RPs are coupled because earthmoving productivity is partially dependent on surface quality. The RPs and their coupling were addressed in four publications by coordinating the mobile base and manipulator control and by using the surrounding terrain shape in automatic blade motion reference computations. Challenges to automatic control emerge from the tracked mobile platform driving on rough terrain while the manipulator tool interacts with the soil. It is shown in the first two publications that coordinating the control of the MM mobile base and blade manipulator subsystems can improve surface quality and productivity by temporarily slowing down the machine when the required manipulator joint rates increase or when the tractive performance reduces. The third publication showed that feedforward–feedback control of the blade manipulator can be used on a real-world bulldozer for accurate terrain shaping. The thesis work culminates in the final publication with an experimental implementation of a semi-autonomous blade control system that continuously maps the worksite terrain and uses it to compute the required blade motion

Methods for the improvement of power resource prediction and residual range estimation for offroad unmanned ground vehicles

Unmanned Ground Vehicles (UGVs) are becoming more widespread in their deployment. Advances in technology have improved not only their reliability but also their ability to perform complex tasks. UGVs are particularly attractive for operations that are considered unsuitable for human operatives. These include dangerous operations such as explosive ordnance disarmament, as well as situations where human access is limited including planetary exploration or search and rescue missions involving physically small spaces. As technology advances, UGVs are gaining increased capabilities and consummate increased complexity, allowing them to participate in increasingly wide range of scenarios. UGVs have limited power reserves that can restrict a UGV’s mission duration and also the range of capabilities that it can deploy. As UGVs tend towards increased capabilities and complexity, extra burden is placed on the already stretched power resources. Electric drives and an increasing array of processors, sensors and effectors, all need sufficient power to operate. Accurate prediction of mission power requirements is therefore of utmost importance, especially in safety critical scenarios where the UGV must complete an atomic task or risk the creation of an unsafe environment due to failure caused by depleted power. Live energy prediction for vehicles that traverse typical road surfaces is a wellresearched topic. However, this is not sufficient for modern UGVs as they are required to traverse a wide variety of terrains that may change considerably with prevailing environmental conditions. This thesis addresses the gap by presenting a novel approach to both off and on-line energy prediction that considers the effects of weather conditions on a wide variety of terrains. The prediction is based upon nonlinear polynomial regression using live sensor data to improve upon the accuracy provided by current methods. The new approach is evaluated and compared to existing algorithms using a custom ‘UGV mission power’ simulation tool. The tool allows the user to test the accuracy of various mission energy prediction algorithms over a specified mission routes that include a variety of terrains and prevailing weather conditions. A series of experiments that test and record the ‘real world’ power use of a typical small electric drive UGV are also performed. The tests are conducted for a variety of terrains and weather conditions and the empirical results are used to validate the results of the simulation tool. The new algorithm showed a significant improvement compared with current methods, which will allow for UGVs deployed in real world scenarios where they must contend with a variety of terrains and changeable weather conditions to make accurate energy use predictions. This enables more capabilities to be deployed with a known impact on remaining mission power requirement, more efficient mission durations through avoiding the need to maintain excessive estimated power reserves and increased safety through reduced risk of aborting atomic operations in safety critical scenarios. As supplementary contribution, this work created a power resource usage and prediction test bed UGV and resulting data-sets as well as a novel simulation tool for UGV mission energy prediction. The tool implements a UGV model with accurate power use characteristics, confirmed by an empirical test series. The tool can be used to test a wide variety of scenarios and power prediction algorithms and could be used for the development of further mission energy prediction technology or be used as a mission energy planning tool

Robotic autonomous systems for earthmoving equipment operating in volatile conditions and teaming capacity: a survey

Abstract There has been an increasing interest in the application of robotic autonomous systems (RASs) for construction and mining, particularly the use of RAS technologies to respond to the emergent issues for earthmoving equipment operating in volatile environments and for the need of multiplatform cooperation. Researchers and practitioners are in need of techniques and developments to deal with these challenges. To address this topic for earthmoving automation, this paper presents a comprehensive survey of significant contributions and recent advances, as reported in the literature, databases of professional societies, and technical documentation from the Original Equipment Manufacturers (OEM). In dealing with volatile environments, advances in sensing, communication and software, data analytics, as well as self-driving technologies can be made to work reliably and have drastically increased safety. It is envisaged that an automated earthmoving site within this decade will manifest the collaboration of bulldozers, graders, and excavators to undertake ground-based tasks without operators behind the cabin controls; in some cases, the machines will be without cabins. It is worth for relevant small- and medium-sized enterprises developing their products to meet the market demands in this area. The study also discusses on future directions for research and development to provide green solutions to earthmoving.</jats:p

Improving perception and locomotion capabilities of mobile robots in urban search and rescue missions

Nasazení mobilních robotů během zásahů záchranných složek je způsob, jak učinit práci záchranářů bezpečnější a efektivnější. Na roboty jsou ale při takovém použití kladeny vyšší nároky kvůli podmínkám, které při těchto událostech panují. Roboty se musejí pohybovat po nestabilních površích, ve stísněných prostorech nebo v kouři a prachu, což ztěžuje použití některých senzorů. Lokalizace, v robotice běžná úloha spočívající v určení polohy robotu vůči danému souřadnému systému, musí spolehlivě fungovat i za těchto ztížených podmínek. V této dizertační práci popisujeme vývoj lokalizačního systému pásového mobilního robotu, který je určen pro nasazení v případě zemětřesení nebo průmyslové havárie. Nejprve je předveden lokalizační systém, který vychází pouze z měření proprioceptivních senzorů a který vyvstal jako nejlepší varianta při porovnání několika možných uspořádání takového systému. Lokalizace je poté zpřesněna přidáním měření exteroceptivních senzorů, které zpomalují kumulaci nejistoty určení polohy robotu. Zvláštní pozornost je věnována možným výpadkům jednotlivých senzorických modalit, prokluzům pásů, které u tohoto typu robotů nevyhnutelně nastávají, výpočetním nárokům lokalizačního systému a rozdílným vzorkovacím frekvencím jednotlivých senzorů. Dále se věnujeme problému kinematických modelů pro přejíždění vertikálních překážek, což je další zdroj nepřesnosti při lokalizaci pásového robotu. Díky účasti na výzkumných projektech, jejichž členy byly hasičské sbory Itálie, Německa a Nizozemska, jsme měli přístup na cvičiště určená pro přípravu na zásahy během zemětřesení, průmyslových a dopravních nehod. Přesnost našeho lokalizačního systému jsme tedy testovali v podmínkách, které věrně napodobují ty skutečné. Soubory senzorických měření a referenčních poloh, které jsme vytvořili pro testování přesnosti lokalizace, jsou veřejně dostupné a považujeme je za jeden z přínosů naší práce. Tato dizertační práce má podobu souboru tří časopiseckých publikací a jednoho článku, který je v době jejího podání v recenzním řízení.eployment of mobile robots in search and rescue missions is a way to make job of human rescuers safer and more efficient. Such missions, however, require robots to be resilient to harsh conditions of natural disasters or human-inflicted accidents. They have to operate on unstable rough terrain, in confined spaces or in sensory-deprived environments filled with smoke or dust. Localization, a common task in mobile robotics which involves determining position and orientation with respect to a given coordinate frame, faces these conditions as well. In this thesis, we describe development of a localization system for tracked mobile robot intended for search and rescue missions. We present a proprioceptive 6-degrees-of-freedom localization system, which arose from the experimental comparison of several possible sensor fusion architectures. The system was modified to incorporate exteroceptive velocity measurements, which significantly improve accuracy by reducing a localization drift. A special attention was given to potential sensor outages and failures, to track slippage that inevitably occurs with this type of robots, to computational demands of the system and to different sampling rates sensory data arrive with. Additionally, we addressed the problem of kinematic models for tracked odometry on rough terrains containing vertical obstacles. Thanks to research projects the robot was designed for, we had access to training facilities used by fire brigades of Italy, Germany and Netherlands. Accuracy and robustness of proposed localization systems was tested in conditions closely resembling those seen in earthquake aftermath and industrial accidents. Datasets used to test our algorithms are publicly available and they are one of the contributions of this thesis. We form this thesis as a compilation of three published papers and one paper in review process

A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

Development of track-driven agriculture robot with terrain classification functionality / Khairul Azmi Mahadhir

Over the past years, many robots have been devised to facilitate agricultural activities (that are labor-intensive in nature) so that they can carry out tasks such as crop care or selective harvesting with minimum human supervision. It is commonly observed that rapid change in terrain conditions can jeopardize the performance and efficiency of a robot when performing agricultural activity. For instance, a terrain covered with gravel produces high vibration to robot when traversing on the surface. In this work, an agricultural robot is embedded with machine learning algorithm based on Support Vector Machine (SVM). The aim is to evaluate the effectiveness of the Support Vector Machine in recognizing different terrain conditions in an agriculture field. A test bed equipped with a tracked-driven robot and three types o f terrain i.e. sand, gravel and vegetation has been developed. A small and low power MEMS accelerometer is integrated into the robot for measuring the vertical acceleration. In this experiment, the vibration signals resulted from the interaction between the robot and the different type of terrain were collected. An extensive experimental study was conducted to evaluate the effectiveness of SVM. The results in terms of accuracy of two machine learning techniques based on terrain classification are analyzed and compared. The results show that the robot that is equipped with an SVM can recognize different terrain conditions effectively. Such capability enables the robot to traverse across changing terrain conditions without being trapped in the field. Hence, this research work contributes to develop a self-adaptive agricultural robot in coping with different terrain conditions with minimum human supervision

Development of a Model-based Control Strategy for Autonomous Vehicle Collision Avoidance

Human inattention is the leading cause of traffic accidents in many regions around the world. Autonomous vehicle technologies are rapidly emerging with the aim to remove the human factor in key driving procedures, such as perception, decision-making, path planning, and control. These technologies are subject to technological, ethical, and social scrutiny; therefore, extensive work is required to instill confidence in the reliability of these automated driving features. One key responsibility of automated driving is in planning and tracking a trajectory to avoid collisions with obstacles, such as other vehicles. One of the foremost challenges in the formulation of a feasible path is considering the dynamics and constraints of the vehicle and the environment. Model predictive control (MPC) is one of the most common control techniques for its ability to handle constraints. For this reason, MPC has been widely studied for path planning and tracking for autonomous vehicles and mobile robots. MPC relies upon an accurate vehicle dynamics model which enables accurate state predictions, thereby resulting in effective control actions to achieve the desired objective. It is challenging, however, to capture all of the details and uncertainties of the dynamics associated with a vehicle. In particular, modeling tire dynamics requires detailed nonlinear models to fully reflect the vehicle behavior. One common technique for motion planning using MPC is to employ artificial potential fields (PFs) which generate an artificial repulsive force from obstacles or road boundaries to influence the controller to track the vehicle along a safe trajectory. Some state-of-the-art PF-based techniques include the PF intensity directly in the MPC objective function, thereby considering the vehicle constraints and dynamics as part of the path planning. In this thesis, an enhanced PF-based motion controller is presented. The control design uses MPC with a detailed dynamics model; the model considers the combined-slip effect on tire forces, nonlinearities, and actuator dynamics. Therefore, it offers an improvement upon prior studies which rely upon simplified dynamics models. Moreover, the PF intensity is included in the objective function, like prior studies, although the PF approximation is further simplified by only considering the lateral component of the repulsive force as part of the latera controller. A separate, novel longitudinal control policy uses the longitudinal component of the PF gradient to regulate the speed setpoint when approaching an obstacle in the same lane; subsequently, proportional-integral-derivative (PID) controllers command axle torque and brake pressure to track the reference speed. The developed controller and dynamics model are validated in both simulation and physical vehicle tests. To emulate the various driving scenarios where avoidance or stopping is required, a virtual driving environment is employed: simulated obstacles are placed in the roadway, the detections of which are sent to the controller. The controller performance is demonstrated in various evasive maneuvers, and in different road conditions