Ground Mobile Robots Research Articles

Evaluating an autonomous electric robot for real farming applications

Mobile robotic technologies are emerging in agriculture in recent years, driven by labor shortages, the need of precision farm management, and the improvement of farmers' quality of life. Mobile robotic technologies, both aerial and ground, have been considered as one of the most important and promising solutions to address agricultural challenges. Nevertheless, the implementation rate in farms is still low. The aim of this study was to evaluate the performance of a mobile agricultural ground robot to assess its possible implementation in the farm environment. The mobility performances as well as the energy consumption of the mobile robot were evaluated considering different interaction systems (teleoperated, autonomous driving) and application areas (weeding, tilling). The results show that the forward speed of the tested mobile robot is affected by the towed load, up to 230 kg, and that the robot could be driven continuously for 2 h in the field without decreasing its performances at a maximum speed between 0.71–0.77 m s-1. The autonomy of the tested robot is limited, but this is mainly due to the battery technology used. The mobile robot effectively tow implements for weeding and tilling operations managing grass with a single pass (40 % weed removal) and improving soil bulk density. The average energy consumption across the different settings was about 1.43 kWh for each hour of use. Moreover, although the RTK-GNSS autonomous navigation used was a low-cost system, the results showed a good accuracy, which allowed the mobile robot to navigate autonomously in the 2 m inter-row of the vineyard. Finally, the working capacity of the mobile robots, considering the implements used, is on average 0.29 ha h-1. This study provides further evidence for the possible use of ground mobile robot in agriculture to carry out autonomous or semi-autonomous operations.

Self-Localization of Anonymous UGVs Using Deep Learning from Periodic Aerial Images for a GPS-Denied Environment

This work concerns the autonomous navigation of non-holonomic ground mobile robots in a GPS-denied environment. The objective was to locate, in a global frame, without GPS, anonymous ground mobile robots starting from two consecutive aerial images captured by a single fixed webcam. The effectiveness of deep learning by a MultiLayer Perceptron in an indexed localization was compared to the methods studied in previous works. The ability of a robot to determine the position of other non-indexed robots was also performed. The structure and parameters of the network and the choice of the points taken into account during the learning phase to obtain a local optimum are presented. The results, obtained from simulated and experimental data, are compared to those obtained with more classical methods for different sampling periods (time between images).

Research on ground mobile robot trajectory tracking control based on MPC and ANFIS

This study focuses on the control strategy for a ground mobile robot (GMR) with independent three-axis six-wheel drive and four-wheel independent steering, performing double lane change trajectory tracking in complex scenarios. Initially, a dynamic model of the six-wheel independent drive and steering GMR was constructed. Utilizing Model Predictive Control (MPC) technology, the challenge of trajectory tracking at low speeds was effectively addressed. For high-speed conditions, by thoroughly analyzing the impact of the predictive time-domain, this study innovatively introduced an Adaptive Neuro-Fuzzy Inference System (ANFIS) to dynamically adjust the prediction horizon of the MPC. A novel trajectory tracking algorithm integrating MPC and ANFIS was developed, with the network structure being trained using backpropagation (BP) method and the least squares method. Compared to traditional MPC, this hybrid strategy significantly improves trajectory tracking accuracy and stability at high speeds, with computational efficiency increased by 48.65%. Additionally, the algorithm demonstrated excellent adaptability and control effectiveness in various rigorous tests, including different speed levels, complex steering paths, load changes, sudden obstacles, and variable terrain. A 70 km/h trajectory tracking experiment on a physical vehicle yielded a root mean square (RMS) error of 0.1904 m, verifying its superior tracking performance and practical reliability. This provides a pioneering solution for high-performance trajectory control of ground mobile robots.

Ground Adaptability of Crawler Mobile Robots with Sub-Crawler Rotary Joint Compliance

Remote-controlled mobile robots are expected to be used in difficult- or impossible-to-access environments for inspection workers and responders, such as in investigations and search activities at accident/disaster sites and inspection/investigation work at plants/infrastructure. Among ground mobile robots, crawler mobile robots with sub-crawlers (also known as ground-adaptive crawler robots) excel at in-ground adaptability and stack escape; however, their operators require advanced remote-control technology and experience. Therefore, the introduction of semi-autonomous control to assist the operator is required. In this study, the principle of the pushing-up sequence and the possibility of mobiligence emerging from interaction with obstacles caused by the robot movement were described. In addition, the sub-crawler rotary joint’s compliance, which significantly contributes to ground adaptability, was hypothesized, and a compliance control system design method that uses the sub-crawler constraint angle as a design condition was proposed. It was confirmed that the model robot for the evaluation, which used the proposed method, could adapt to unknown obstacles without measuring their height and shape and traverse them based on experimental results. In addition, based on the numerical calculation results, it was determined that the optimum solution for the restriction angle of the sub-crawler was approximately 35°–50° from the perspective of propulsive force and tumble stability.

WheTLHLoc 4W: Small‐scale inspection ground mobile robot with two tracks, two rotating legs, and four wheels

AbstractWheTLHLoc 4W is the second version of a hybrid leg–wheel–track small‐scale ground mobile robot, developed for surveillance and inspection applications. It differs from its predecessor, WheTLHLoc 2W, in two main aspects: the number of wheels (four instead of two), and the new leg design. The overall size of the robot varies depending on the configuration: 500 × 420 × 140 mm (length × width × height) in tracked mode with lowered legs, 315 × 420 × 310 mm in tracked mode with raised legs, and 430 × 420 × 260 mm in wheeled mode. The robot is sufficiently small to explore narrow spaces, both indoors and outdoors, switching between wheeled locomotion on compact surfaces and tracked locomotion on irregular and yielding terrain. Due to its small length, the robot can stand on one tread of a stair. Nevertheless, it is capable of climbing stairs, and the step height limit has been augmented from 165 mm to 180 mm (+9.1%) with respect to the first version, keeping constant the robot overall size. This enhancement has been achieved through a mechanical redesign of the two rotating legs: each leg now features a pair of smaller wheels, actuated by the same actuator through a gear train, instead of a single larger wheel. The significant increase in the maximum step height expands the robot's capabilities for indoor environments, as stairs with a rise higher than 180 mm are extremely uncommon. The paper discusses the mechanical design of WheTLHLoc 4W, the trajectory planning for step/stair climbing, and provides an analytical comparison between the novel architecture and the first version in terms of stability against overturn and slippage. These analytical results are then experimentally verified. The findings demonstrate that the new leg design not only increases the maximum step height (+9.1%), but also reduces the minimum friction coefficient required for successful stair climbing (−19.1% for the maximum step).

Secure learning-based coordinated UAV–UGV framework design for medical waste transportation

A cost-effective solution with less human involvement must be developed for medical waste (MW) transportation. A learning-based coordinated unmanned aerial vehicle–unmanned ground vehicle (UAV–UGV) (CUU) framework, currently unavoidable use, with a transfer learning algorithm is suggested. A transfer learning algorithm is implemented for collision-free optimal path planning. In the framework, mobile ground robots collect medical waste from waste disposal centers through the pick-and-place technique. Then, networked drones lift the collected medical waste and fly through a predefined optimal trajectory. The framework considers the dynamic behavior of the environment and explores the actions for picking, placing, and dropping medical waste. A deep reinforcement learning mechanism has been incorporated for each successful or unsuccessful action by the framework to provide the rewards. With optimal policies, the coordinated UAV and UGV change their actions in dynamic conditions. An optimal cost of transportation of medical waste by the proposed framework is created by considering the weight of MW packets as the payload capacity of a CUU framework, the cost of steering the UAV and UGV, and the time required to transport the MW. The effectiveness of the CUU framework for MW transportation has been tested using MATLAB. The MW transportation data have been encrypted using an encryption key for security and authenticity.

Optimization-based trajectory planning for transport collaboration of heterogeneous systems

Abstract This paper describes an optimization-based trajectory planning scheme for handing over an object between a quadrotor and a wheeled robot in a transportation scenario. Concretely, a quadrotor should pick up an object from a moving ground mobile robot and deliver it to its destination. An optimization framework based on discrete mechanics and complementarity constraints is utilized here to jointly ensure dynamic feasibility and determine the position, timing, and coordination of the handover autonomously. Cooperative trajectories of the heterogeneous robot system can be generated simultaneously to satisfy different requirements by adjusting the objective function and constraints. The proposed planning scheme provides a novel paradigm combining trajectory planning and handover decision-making within an optimal control problem.

Simulation Based Study Of Ground Mobile Robots For Inspection And Control Of The Bridge Assembly Process

Nowadays, the inspection of bridges poses a significant challenge for safeguarding critical infrastructures. Recent advancements have seen the integration of robots to replace human personnel in hazardous tasks. These robots, whether operating autonomously or through tele-operation, play a crucial role in monitoring structures and infrastructure, ensuring secure access to areas such as manholes on decks and box girder bridges. Specifically designed for environments deemed dangerous, difficult, or inaccessible to humans, ground mobile robots equipped with appropriate sensors are increasingly deployed for inspection purposes. This paper addresses simulation tests conducted by a hybrid mobile robot. Its compact design and maneuverability enable it to navigate through obstacles, making it suitable for inspecting railway or highway bridge decks as well as confined spaces within box girder bridges. The study includes a survey of existing bridges highlighting key issues to be addressed and presents simulation results for the hybrid rover, which has been developed and equipped with sensors tailored to fulfill inspection tasks within a selected case study.

Online Odometry Calibration for Differential Drive Mobile Robots in Low Traction Conditions with Slippage

This paper addresses a systematic method for odometry calibration of a differential-drive mobile robot moving on arbitrary paths in the presence of slippage and an algorithm encoding it which is well fit for online applications. It exploits the redundancy of sensors commonly available on ground mobile robots, such as encoders, gyroscopes, and IMU, to promptly detect slippage phenomena during the calibration process and effectively address their impact on odometry. The proposed technique has been validated through exhaustive numerical simulations and compared with other available odometry calibration methods. The simulation results confirm that the proposed methodology mitigates the impact of poor calibration, conducted without considering possible slipping phenomena, on reaching a target position, reducing the error by up to a maximum of 35 times. This restores the robot’s performance to a calibration condition close to that of a slip-free scenario, confirming the effectiveness of the approach and its robustness against slippage phenomena.

An analysis of the effects of water regime on grapevine canopy status using a UAV and a mobile robot

In this paper, we propose a novel approach for analyzing the effects of water regime on grapevine canopy status using robotics as an aid for monitoring and mapping. Data from an unmanned aerial vehicle (UAV) and a ground mobile robot are used to obtain multispectral images and multiple vegetation indexes, and the 3D reconstruction of the canopy, respectively. Unlike previous works, sixty vegetation indexes are computed precisely by using the projected area of the vineyard point cloud as a mask. Extensive experimental tests on repeated plots of Pinot gris vines show that the GDVI, PVI, and TGI vegetation indexes are positively correlated with the water potential: GDVI (R2=0.90 and 0.57 for the stem and pre-dawn water potential, respectively), PVI (R2=0.90 and 0.57), TGI (R2=0.87 and 0.77). Furthermore, the canopy volume and the canopy area projected on the ground are impacted by the water status, as well as stem and pre-dawn water potential measurements. The results obtained in this work demonstrate the feasibility of the proposed approach and the potential of robotic technologies, supporting precision viticulture.

Velocity obstacle-based trajectory planner for anthropomorphic arms

The Velocity Obstacle algorithm is one of the most popular and studied decentralized trajectory planning methods for multi-agent systems moving in dynamic environments. It has been successfully used in a multitude of real and simulated scenarios for collision-free maneuvers of ground and aerial mobile robots, proving to be versatile, adaptable, and efficient even with large numbers of holonomic and nonholonomic autonomous agents. In this paper, we address the problem of adapting Velocity Obstacles for planning collision-free trajectories for anthropomorphic arms. We show MATLAB® simulations and experimental tests on a real UR5 robotic arm by Universal Robotics in the presence of moving obstacles. Finally, we compared the collision-free trajectories of the proposed planner to those obtained by using Artificial Potential Fields: our approach gives better results in terms of both smoothness and time employed for reaching the target position.

Towards autonomous mapping in agriculture: A review of supportive technologies for ground robotics

This paper surveys the supportive technologies currently available for ground mobile robots used for autonomous mapping in agriculture. Unlike previous reviews, we describe state-of-the-art approaches and technologies aimed at extracting information from agricultural environments, not only for navigation purposes but especially for mapping and monitoring. The state-of-the-art platforms and sensors, the modern localization techniques, the navigation and path planning approaches, as well as the potentialities of artificial intelligence towards autonomous mapping in agriculture are analyzed. According to the findings of this review, many examples of recent mobile robots provide full navigation and autonomous mapping capability. Significant resources are currently devoted to this research area, in order to further improve mobile robot capabilities in this complex and challenging field.

Distributed Weighted Coverage for Multi-Robot Systems in Non-Convex Environment

Multi-robot coverage systems are widely used in operations such as environmental monitoring, disaster rescue, and pollution prevention. This study considers inherent positioning errors in positioning systems and ground mobile robots with limited communication distance and poor quality in practice. A centroidal Voronoi tessellation algorithm-based formation control technology for multi-robots is optimized. First, by constructing buffered Voronoi cells (BUVCs) for each robot, the collision avoidance ability of the multi-robot formation movement is improved. Next, the formation control problem of multi-robots in a limited communication range and non-convex environment is realized via discrete Voronoi partitioning, a communication distance constraint, and an obstacle avoidance strategy. Simulation and experiment results demonstrate that the proposed method can effectively solve the position generation problem of multi-robot coverage systems in a non-convex environment with actual sizes of the robots and positioning system errors and can further improve the collision avoidance performance of robots and the robustness of BUVC algorithms.

비정형 환경의 모바일 로봇을 위한 보조 도착점 기반 계층적 경로계획 프레임워크

Navigating unstructured environments poses challenges for mobile robots. Because such environments are partially observable, ground mobile robots must consider both global traversability and local obstacles. In this paper, we proposed a subgoal-based hierarchical motion planning framework for mobile robots to navigate unstructured environments. This framework comprises a global path planner, a subgoal planner, and a local path planner. This framework enables the mobile robot to effectively avoid local obstacles that cannot be represented on the global map. The hierarchical framework plans subgoals in local coordinates to avoid obstacles when following the planned global path. The local planner utilizes subgoals to generate local paths. To evaluate the proposed framework, we constructed a simulated unstructured terrain environment with diverse obstacles. The hierarchical framework exhibited better and safer quantitative performance than a conventional global path-based method, and the mobile robot successfully navigated unstructured environments by using the hierarchical framework while avoiding obstacles in local coordinates in real time.

MONITORING OF DYNAMIC OBJECTS ON A 2D OCCUPANCY MAP USING NEURAL NETWORKS AND MULTIMODAL DATA

Abstract. The paper deals with the construction of dynamic occupancy maps, where the grid cell can contain not only information about the presence or absence of an obstacle, but also information about its velocity. We propose a multimodal approach to constructing 2D dynamic occupancy maps from LiDAR point clouds and camera images. The approach involves building a static occupancy map from LiDAR data and then adding information about cell velocities based on neural network instance segmentation and object tracking in monocular onboard camera images. Pedestrians and vehicles were considered as dynamic objects. One of the important stages is the projection of the object masks found in the images onto a 2D occupancy map. We compared the proposed approach with the classical method of constructing dynamic occupancy maps from LiDAR data based on the Monte Carlo method. An experimental quality evaluation of the approaches was carried out using the popular Mapillary Vistas and Waymo Open Datasets containing street scenes and a large number of dynamic objects. We demonstrate that the considered approaches can work in real time, which indicates the possibility of their application as part of the on-board control systems of autonomous cars or ground mobile robots. The software implementation of the proposed dynamic occupancy reconstruction approach is publicly available at the link: https://github.com/andrey1908/nn-dynamic-occupancy.

Internet of Underground Things in Agriculture 4.0: Challenges, Applications and Perspectives.

Internet of underground things (IoUTs) and wireless underground sensor networks (WUSNs) are new technologies particularly relevant in agriculture to measure and transmit environmental data, enabling us to optimize both crop growth and water resource management. The sensor nodes can be buried anywhere, including in the passage of vehicles, without interfering with aboveground farming activities. However, to obtain fully operational systems, several scientific and technological challenges remain to be addressed. The objective of this paper is to identify these challenges and provide an overview of the latest advances in IoUTs and WUSNs. The challenges related to the development of buried sensor nodes are first presented. The recent approaches proposed in the literature to autonomously and optimally collect the data of several buried sensor nodes, ranging from the use of ground relays, mobile robots and unmanned aerial vehicles, are next described. Finally, potential agricultural applications and future research directions are identified and discussed.

Learning‐based model predictive control for improved mobile robot path following using Gaussian processes and feedback linearization

AbstractThis paper proposes a high‐performance path following algorithm that combines Gaussian processes (GP) based learning and feedback linearization (FBL) with model predictive control (MPC) for ground mobile robots operating in off‐road terrains, referred to as GP‐FBLMPC. The algorithm uses a nominal kinematic model and learns unmodeled dynamics as GP models by using observation data collected during field experiments. Extensive outdoor experiments using a Clearpath Husky A200 mobile robot show that the proposed GP‐FBLMPC algorithm's performance is comparable to existing GP learning‐based nonlinear MPC (GP‐NMPC) methods with respect to the path following errors. The advantage of GP‐FBLMPC is that it is generalizable in reducing path following errors for different paths that are not included in the GP models training process, while GP‐NMPC methods only work well on exactly the same path on which GP models are trained. GP‐FBLMPC is also computationally more efficient than the GP‐NMPC because it does not conduct iterative optimization and requires fewer GP models to make predictions over the MPC prediction horizon loop at every time step. Field tests show the effectiveness and generalization of reducing path following errors of the GP‐FBLMPC algorithm. It requires little training data to perform GP modeling before it can be used to reduce path‐following errors for new, more complex paths on the same terrain (see video at https://youtu.be/tC09jJQ0OXM).

Global Path Planning Method Based on a Modification of the Wavefront Algorithm for Ground Mobile Robots

This article is focused on the problematics of path planning, which means finding the optimal path between two points in a known environment with obstacles. The proposed path-planning method uses the wavefront algorithm, and two modifications are implemented and verified. The first modification is the removal of redundant waypoints. The first modification is applied because the wavefront algorithm generates redundant waypoints. These waypoints cause unnecessary changes in the direction of movement. The second one is smoothing the generated trajectory using B-spline curves. The reason for applying the second modification is that trajectory generated by the wavefront algorithm is in the form of the polyline, which is inadequate in terms of the smoothness of the robot’s motion. The verification of the proposed method is performed in environments with different densities of obstacles compared with standard Dijkstra’s and A* algorithms.

Distributed Optimal Control Framework for High-Speed Convoys: Theory and Hardware Results

Practical deployments of coordinated fleets of mobile robots in different environments have revealed the benefits of maintaining small distances between robots, especially as they move at higher speeds. However, this is counter-intuitive in that as speed increases, reducing the amount of space between robots also reduces the time available to the robots to respond to sudden motion variations in surrounding robots. However, in certain examples, the benefits in performance due to traveling at closer distances can outweigh the potential instability issues, for instance, autonomous trucks on highways that optimize energy by vehicle “drafting” or smaller robots in cluttered environments that need to maintain close, line of sight communication, etc. To achieve this kind of closely coordinated fleet behavior, this work introduces a model predictive optimal control framework that directly takes non-linear dynamics of the vehicles in the fleet into account while planning motions for each robot. The robots are able to follow each other closely at high speeds by proactively making predictions and reactively biasing their responses based on state information from the adjacent robots. This control framework is naturally decentralized and, as such, is able to apply to an arbitrary number of robots without any additional computational burden. We show that our approach is able to achieve lower inter-robot distances at higher speeds compared to existing controllers. We demonstrate the success of our approach through simulated and hardware results on mobile ground robots.

Human-assisted robotic detection of foreign object debris inside confined spaces of marine vessels using probabilistic mapping

Many complex vehicular systems, such as large marine vessels, contain confined spaces like water tanks, which are critical for the safe functioning of the vehicles. It is particularly hazardous for humans to inspect such spaces due to limited accessibility, poor visibility, and unstructured configuration. While robots provide a viable alternative, they encounter the same set of challenges in realizing robust autonomy. In this work, we specifically address the problem of detecting foreign object debris (FODs) left inside the confined spaces using a visual mapping-based system that relies on Mahalanobis distance-driven comparisons between the nominal and online maps for local outlier identification. The identified outliers, corresponding to candidate FODs, are used to generate waypoints that are fed to a mobile ground robot to take camera photos. The photos are subsequently labeled by humans for final identification of the presence and types of FODs, leading to high detection accuracy while mitigating the effect of recall–precision tradeoff. Preliminary simulation studies, followed by extensive physical trials on a prototype tank, demonstrate the capability and potential of our FOD detection system.