Low-cost Robot Research Articles

Low-Cost Robotic Manipulation of Live Microtissues for Cancer Drug Testing.

A low-cost robot for handling microtissues and catalyzing their use in cancer drug evaluation and personalized oncology.

The role of low-cost robots in the future of spaceflight.

Lessons from the CubeSat and Mars Exploration programs may guide the infusion of robotics for planetary science and exploration.

A Cobot in the Vineyard: Computer Vision for Smart Chemicals Spraying

Precision agriculture (PA) is a management concept that makes use of digital techniques to monitor and optimise agricultural production processes and represents a field of growing economic and social importance. Within this area of knowledge, there is a topic not yet fully explored: outlining a road map towards the definition of an affordable cobot solution (i.e., a low-cost robot able to safely coexist with humans) able to perform automatic chemical treatments. The present study narrows its scope to viticulture technologies, and targets small/medium-sized winemakers and producers, for whom innovative technological advancements in the production chain are often precluded by financial factors. The aim is to detail the realization of such an integrated solution and to discuss the promising results achieved. The results of this study are: (i) The definition of a methodology for integrating a cobot in the process of grape chemicals spraying under the constraints of a low-cost apparatus; (ii) the realization of a proof-of-concept of such a cobotic system; (iii) the experimental analysis of the visual apparatus of this system in an indoor and outdoor controlled environment as well as in the field.

Impact of Low-Cost Robot EducThermoBot vs. EXAO on Students' Motivation and Learning in Physical Sciences in Morocco

Teaching physical sciences presents a significant challenge in various countries, including Morocco, where student performance remains a subject of concern. This study aims to investigate the impact of introducing educational robotics as compared to computer-assisted experiments (EXAO) on students' motivation and learning in physical sciences. We divided a group of 120 middle-school students into two cohorts: one used the cost-effective educational robot, EducThermoBot, as an experimental group, while the other employed traditional EXAO as a control group. The findings demonstrate that the integration of educational robotics has a noteworthy and positive impact on students' motivation, whereas the overall academic performance exhibited no significant disparities between the two cohorts. Notwithstanding some limitations, this research offers valuable insights to enhance pedagogical practices by promoting the integration of educational robotics into the teaching of physical sciences in Morocco.

Landmark-based distributed topological mapping and navigation in GPS-denied urban environments using teams of low-cost robots

In this paper, we address the problem of autonomous multi-robot mapping, exploration and navigation in unknown, GPS-denied indoor or urban environments using a team of robots equipped with directional sensors with limited sensing capabilities and limited computational resources. The robots have no a priori knowledge of the environment and need to rapidly explore and construct a map in a distributed manner using existing landmarks, the presence of which can be detected using onboard senors, although little to no metric information (distance or bearing to the landmarks) is available. In order to correctly and effectively achieve this, the presence of a necessary density/distribution of landmarks is ensured by design of the urban/indoor environment. We thus address this problem in two phases: (1) During the design/construction of the urban/indoor environment we can ensure that sufficient landmarks are placed within the environment. To that end we develop a filtration-based approach for designing strategic placement of landmarks in an environment. (2) We develop a distributed algorithm which a team of robots, with no a priori knowledge of the environment, can use to explore such an environment, construct a topological map requiring no metric/distance information, and use that map to navigate within the environment. This is achieved using a topological representation of the environment (called a Landmark Complex), instead of constructing a complete metric/pixel map. The representation is built by the robot as well as used by them for navigation through a balanced strategy involving exploration and exploitation. We use tools from homology theory for identifying “holes” in the coverage/exploration of the unknown environment and hence guide the robots towards achieving a complete exploration and mapping of the environment. Our simulation results demonstrate the effectiveness of the proposed metric-free topological (simplicial complex) representation in achieving exploration, localization and navigation within the environment.

Marine algae inspired dispersion of swarm robots with binary sensory information

The dynamics of swarm robotic systems are complex and often nonlinear. One key issue is to design the controllers of a large number of simple, low-cost robots so that emergence can be observed. This paper presents a sensor and computation-friendly controller for swarm robotic systems inspired by the mechanisms observed in algae. The aim is to achieve uniform dispersion of robots by mimicking the circular movement observed in marine algae systems. The proposed controller utilizes binary sensory information (i.e., see or not see) to guide the robots’ motion. By moving circularly and switching the radii based on the perception of other robots in their line of sight, the robots imitate the repulsion behavior observed in algae. The controller relies solely on binary-state sensory input, eliminating the need for additional memory or communication. Up to 1024 simulated robots are used to validate the effectiveness of the dispersion controller, while experiments with 30 physical robots demonstrate the feasibility of the proposed approach.

A Dual-Arm Robot That Manipulates Heavy Plates With the Support of a Vacuum Lifter

A vacuum lifter is widely used to hold and pick up large, heavy, and flat objects. Conventionally, when using a vacuum lifter, a human worker watches the state of a running vacuum lifter and adjusts the object&#x2019;s pose to maintain balance. In this work, we propose using a dual-arm robot to replace the human workers and develop planning and control methods for a dual-arm robot to raise a heavy plate with the help of a vacuum lifter. The methods help the robot determine its actions by considering the vacuum lifer&#x2019;s suction position and suction force limits. The essence of the methods is two-fold. First, we build a Manipulation State Graph (MSG) to store the weighted logical relations of various plate contact states and robot/vacuum lifter configurations, and search the graph to plan efficient and low-cost robot manipulation sequences. Second, we develop a velocity-based impedance controller to coordinate the robot and the vacuum lifter when lifting an object. With its help, a robot can follow the vacuum lifter&#x2019;s motion and realize compliant robot-vacuum lifter collaboration. Real-world experiments are carried out to investigate the proposed planning and control methods. The results show that a robot can effectively and flexibly work together with a vacuum lifter to manipulate large and heavy plate-like objects with the methods&#x2019; support. <i>Note to Practitioners</i>&#x2014;This paper is motivated by the vacuum lifters used for transporting heavy plates in a factory that produces building materials. In the factory, a human worker attaches the suction cup of a vacuum lifter to a plate and controls the vacuum lifter to pull the plate up. Meanwhile, another human worker moves and lifts the plate to a goal pose, following the vacuum lifter while maintaining balance. The job is dangerous as the plate is heavy, and the vacuum lifter is not always strong enough to hold the plate firmly. Inspired by the usage and safety problem, we in this paper develop a planning and control method for a dual-arm robot to replace humans. The robot coordinates its motion to work with the vacuum lifter and performs lifting tasks. The vacuum lifter could remain operated by a human worker or be actuated by signals from the dual-arm robot or other third-party machines. The work is complementary to our previous study that developed planners for robots to use pulley blocks. They together provide extensive knowledge for using low-payload collaboratively robots to manipulate heavy plates.

ROS-based Controller for a Two-Wheeled Self-Balancing Robot

In this article, a controller based on a Robot Operating System (ROS) for a two-wheeled self-balancing robot is designed. The proposed ROS architecture is open, allowing the integration of different sensors, actuators, and processing units. The low-cost robot was designed for educational purposes. It used an ESP32 microcontroller as the central unit, an MPU6050 Inertial Measurement Unit sensor, DC motors with encoders, and an L298N integrated circuit as a power stage. The mathematical model is analyzed through Newton-Euler and linearized around an equilibrium point. The control objective is to self-balance the robot to the vertical axis in the presence of disturbances. The proposed control is based on a bounded saturation, which is lightweight and easy to implement in embedded systems with low computational resources. Experimental results are performed in real-time under regulation, conditions far from the equilibrium point, and rejection of external disturbances. The results show a good performance, thus validating the mechanical design, the embedded system, and the control scheme. The proposed ROS architecture allows the incorporation of different modules, such as mapping, autonomous navigation, and manipulation, which contribute to studying robotics, control, and embedded systems.

A low-cost table-top robot platform for measurement science education in robotics and artificial intelligence

Robotics and artificial intelligence represent highly interdisciplinary fields, which – in particular on the bachelor level - makes providing a strong fundamental background in the education challenging. With respect to lab exercises in measurement, one approach to provide interdisciplinary hands-on experience is to embed experiments for measurement science in a robotic context. We present a low-cost robot platform that can be used to address several measurement science and sensor topics, and also other aspects such as machine learning, actuators and mechanics. The 3D printed chassis can be equipped with different sensors for environment perception but also be adapted to different embedded PC platforms. In order to also introduce concepts of robot simulation and realization approaches in a hands-on fashion, the table-top robot is also available as a digital twin in the simulation environments Gazebo and CoppeliaSim®, where, e.g., limitations of simulations and required adaption of models to consider non ideal effects of sensors can be studied.

Computational Systems Design of Low-Cost Lightweight Robots

With the increased demand for customisation, developing task-specific robots for industrial and personal applications has become essential. Collaborative robots are often preferred over conventional industrial robots in human-centred production environments. However, fixed architecture robots lack the ability to adapt to changing user demands, while modular, reconfigurable robots provide a quick and affordable alternative. Standardised robot modules often derive their characteristics from conventional industrial robots, making them expensive and bulky and potentially limiting their wider adoption. To address this issue, the current work proposes a top-down multidisciplinary computational design strategy emphasising the low cost and lightweight attributes of modular robots within two consecutive optimisation problems. The first step employs an informed search strategy to explore the design space of robot modules to identify a low-cost robot architecture and controller. The second step employs dynamics-informed structural optimisation to reduce the robot’s net weight. The proposed methodology is demonstrated on a set of example requirements, illustrating that (1) the robot modules allow exploring non-intuitive robot architectures, (2) the structural mass of the resulting robot is 16 % lower compared to a robot designed using conventional aluminium tubes, and (3) the designed modules ensure the physical feasibility of the robots produced.

An Experimental Methodology for Introducing Educational Robotics and Storytelling in Therapeutical Activities for Children with Neurodevelopmental Disorders

Educational robotics is a valuable tool in education and therapy for children with neurodevelopmental disorders (NDD), especially when introduced in activities, combined with gamification and storytelling elements. However, the lack of familiarity of therapists with the technologies involved makes their widespread introduction difficult and leads to case-specific rather than more generalizable methods. In this paper, we present an experimental methodology which provides a guide for the introduction of these elements in therapeutical activities with children with NDD. Providing a common framework reduces the gap between the different expertise of therapists, educators, and engineers. While establishing a common vocabulary and objectives, the methodology provides a guide for designing activities and evaluating their therapeutic effectiveness. We provide an example with a pilot study using a low-cost robot (Ozobot) in a therapeutic environment. Results regarding the children’s task involvement, level of attention, and use of social skills were positive. In addition, the attitude of some children changed throughout the sessions, improving frustration tolerance. The discussion of the pilot study provides clues for improving future implementations of the presented methodology, which serves as a framework for the design of future experiments that include therapeutic activities with educational robotics, gamification, and storytelling.

Indoor WiFi-Beacon Dataset Construction Using Autonomous Low-Cost Robot for 3D Location Estimation

Datasets used for artificial-neural-network and machine-learning applications play a vital role in the research and application of such techniques in solving real-life problems. The construction and availability of large datasets to be used in the off-line phase of ANN training is usually a crucial and time-consuming step towards system construction. In this work, a framework for autonomous construction of a diverse, extensive, and open dataset* with built-in redundancy is demonstrated. As part of the framework, a low-cost robot using off-the-shelf components is built that constructs the dataset autonomously. The robot includes a controller network with multiple WiFi-transceiver nodes for collecting received-signal-strength indicators (RSSIs) at various elevation points throughout the building. All nodes are configured with direct internet access to streamline the data collection towards an online database that is constructed as part of this framework. Preliminary validation and analysis of the dataset are discussed, and an exploration of the application domain of the dataset is carried out. Moreover, this paper investigates the effect of the height of the hand-held mobile WiFi antenna attached to the robot on the received power strength of the WiFi signal.

HeRo 2.0: a low-cost robot for swarm robotics research

The current state of electronic component miniaturization coupled with the increasing efficiency in hardware and software allow the development of smaller and compact robotic systems. The convenience of using these small, simple, yet capable robots has gathered the research community’s attention towards practical applications of swarm robotics. This paper presents the design of a novel platform for swarm robotics applications that is low cost, easy to assemble using off-the-shelf components, and deeply integrated with the most used robotic framework available today: ROS (Robot Operating System). The robotic platform is entirely open, composed of a 3D printed body and open-source software. We describe its architecture, present its main features, and evaluate its functionalities executing experiments using a couple of robots. Results demonstrate that the proposed mobile robot is capable of performing different swarm tasks, given its small size and reduced cost, being suitable for swarm robotics research and education.

Low-Cost Robot for Agricultural Image Data Acquisition

More sustainable technologies in agriculture are important not only for increasing crop yields, but also for reducing the use of agrochemicals and improving energy efficiency. Recent advances rely on computer vision systems that differentiate between crops, weeds, and soil. However, manual dataset capture and annotation is labor-intensive, expensive, and time-consuming. Agricultural robots provide many benefits in effectively performing repetitive tasks faster and more accurately than humans, and despite the many advantages of using robots in agriculture, the solutions are still often expensive. In this work, we designed and built a low-cost autonomous robot (DARob) in order to facilitate image acquisition in agricultural fields. The total cost to build the robot was estimated to be around $850. A low-cost robot to capture datasets in agriculture offers advantages such as affordability, efficiency, accuracy, security, and access to remote areas. Furthermore, we created a new dataset for the segmentation of plants and weeds in bean crops. In total, 228 RGB images with a resolution of 704 × 480 pixels were annotated containing 75.10% soil area, 17.30% crop area and 7.58% weed area. The benchmark results were provided by training the dataset using four different deep learning segmentation models.

Jabuti Project: Maze Solver Micromouse Robot

The present article discusses the development of the Jabuti Project, which consists of a micromouse, a maze solver robot. The project mainly aims to build a low-cost robot and to assist university students in the practical study of robotics. To solve the maze, a wall-follower algorithm was implemented for anarduino, and stepper motors constituted a PCB of the project to provide more stability to the robot, infrared sensors for area recognition, and a caster wheel to direct it. In conclusion, the circuit parts will be estimated to be assembled and integrated to implement the developed logic with tests in the physical prototype.

Jabuti Project: Maze Solver Micromause Robot

The present article discusses the development of the Jabuti Project, which consists of a micromouse, a maze solver robot. The project mainly aims to build a low-cost robot and to assist university students in the practical study of robotics. To solve the maze, a wall-follower algorithm was implemented for anarduino, and stepper motors constituted a PCB of the project to provide more stability to the robot, infrared sensors for area recognition, and a caster wheel to direct it. In conclusion, the circuit parts will be estimated to be assembled and integrated to implement the developed logic with tests in the physical prototype.

Artificial Intelligence Task Planning of Cooperating Low-Cost Mobile Manipulators: A Case Study on a Fully Autonomous Manufacturing Application

Through the highly innovative processes introduced by the movement Industry 4.0, application of fully autonomous working processes is ready to be integrated into real-world manufacturing applications. While many tasks still require the human to handle particular parts or perform certain steps in the production chain, partial assembly may be performed by cooperating mobile manipulators in the near future. However, many smaller companies already fail to adapt to the new trends, as Industry 4.0, in spite of its beneficial aspects, is costly to implement, particularly in still low-automated sectors. In this work, we show how artificial intelligence task planning may be integrated with computer vision systems to achieve a fully autonomous manufacturing process. We also show how low-cost robots are adapted to process demands on diverse system levels.

Intelligent SLAM Algorithm Fusing Low-Cost Sensors at Risk of Building Collapses

When firefighters search inside a building that is at risk of collapse due to abandonment or disasters such as fire, they use old architectural drawings or a simple monitoring method involving a video device attached to a robot. However, using these methods, the disaster situation inside a building at risk of collapse is difficult to detect and identify. Therefore, we investigate the generation of digital maps for a disaster site to accurately analyze internal situations. In this study, a robot combined with a low-cost camera and two-dimensional light detection and ranging (2D-lidar) traverses across a floor to estimate the location of obstacles while drawing an internal map of the building. We propose an algorithm that detects the floor and then determines the possibility of entry, tracks collapses, and detects obstacles by analyzing patterns on the floor. The robot’s location is estimated, and a digital map is created based on Hector simultaneous localization and mapping (SLAM). Subsequently, the positions of obstacles are estimated based on the range values detected by 2D-lidar, and the position of the obstacles are identified on the map using the map update method in semantic SLAM. All equipment are implemented using low-specification devices, and the experiments are conducted using a low-cost robot that affords near-real-time performance. The experiments are conducted in various actual internal environments of buildings. In terms of obstacle detection performance, almost all obstacles are detected, and their positions identified on the map with a high accuracy of 89%.

SphereVLAD++: Attention-Based and Signal-Enhanced Viewpoint Invariant Descriptor

LiDAR-based localization approach is a fundamental module for large-scale navigation tasks, such as last-mile delivery and autonomous driving, and localization robustness highly relies on viewpoints and 3D feature extraction. Our previous work provides a viewpoint-invariant descriptor to deal with viewpoint differences; however, the global descriptor suffers from a low signal-noise ratio in unsupervised clustering, reducing the distinguishable feature extraction ability. In this work, we develop SphereVLAD++, an attention-enhanced viewpoint invariant place recognition method. SphereVLAD++ projects the point cloud on the spherical perspective for each unique area and captures the contextual connections between local features and their dependencies with global 3D geometry distribution. In return, clustered elements within the global descriptor are conditioned on local and global geometries and support the original viewpoint-invariant property of SphereVLAD. In the experiments, we evaluated the localization performance of SphereVLAD++ on both the public <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">KITTI360</i> dataset and self-generated datasets from the city of Pittsburgh. The experiment results show that SphereVLAD++ outperforms all relative state-of-the-art 3D place recognition methods under small or even totally reversed viewpoint differences and shows 7.06% and 28.15% successful retrieval rates with better than the second best. Low computation requirements and high time efficiency also help its application for low-cost robots.

Advanced Motor Control for Improving the Trajectory Tracking Accuracy of a Low-Cost Mobile Robot

Accurate trajectory tracking is a paramount objective when a mobile robot must perform complicated tasks. In high-speed movements, time delays appear when reaching the desired position and orientation, as well as overshoots in the changes of orientation, which prevent the execution of some tasks. One of the aspects that most influences the tracking performance is the control system of the actuators of the robot wheels. It usually implements PID controllers that, in the case of low-cost robots, do not yield a good tracking performance owing to friction nonlinearity, hardware time delay and saturation. We propose to overcome these problems by designing an advanced process control system composed of a PID controller plus a prefilter combined with a Smith predictor, an anti-windup scheme and a Coulomb friction compensator. The contribution of this article is the motor control scheme and the method to tune the parameters of the controllers. It has been implemented in a well-known low-cost small mobile robot and experiments have been carried out that demonstrate the improvement achieved in the performance by using this control system.