Mobile Robot Applications Research Articles

Analysis and application research of mobile robot navigation related technologies

With social progress and technological development, mobile robots have played an important role in industries, medical care, safety, home and other fields due to their advantages of saving labor costs, reducing personnel work intensity, and avoiding potential job hazards. Especially in indoor environments, such as industrial indoor operations, warehousing and logistics distribution, indoor safety patrols, and other application scenarios, mobile robots have highlighted inestimable application value. Intelligent mobile robot is an important tool in the field of service and automation, and robot navigation technology is an important technology for intelligent mobile robot to achieve self-positioning, robot mapping and path planning. This paper expounds three technologies of mobile robot navigation mapping: visual mapping and positioning, lidar mapping and positioning, and sensor fusion mapping and positioning. After that, the method of path planning of the mobile robot is analyzed. Finally, the application of intelligent mobile robots in factory automation and supermarket guidance is pointed out, and the development trend of intelligent mobile robots is prospected.

Approach toward the application of mobile robots in laser materials processing

Laser-based production systems have become more and more popular in recent years due to their potential to achieve high precision and accuracy in a wide range of different applications. However, the kinematic systems used for laser materials processing (LMP) are often inherited from other production technologies such as milling. The use of mobile robots (MRs) equipped with laser processing optics could disprove the current paradigm of adapted kinematic systems: scaling the size of the material processing system with the size of the components being processed and, thus, the resources used. The trend of autonomous MRs replacing classical kinematic systems in the field of material handling in industrial applications has been evident for years due to their higher flexibility, efficiency, and lower operating costs. In this paper, the prototype of a corresponding MR system is presented. In addition, the general design of the MR is presented. One challenge is the accuracy of an MR; for a common LMP such as laser cutting, the MR must be able to follow a predefined trajectory as accurately as possible. For this purpose, two different measurement systems are presented and compared. To demonstrate the potential of the mobile robot, an exemplary LMP process is also performed and evaluated. Finally, possibilities for improvement or further development, such as integration of scanner optics or the use of several autonomous MRs to increase productivity, are shown.

A novel directional sampling-based path planning algorithm for ambient intelligence navigation scheme in autonomous mobile robots

Path planning algorithms determine the performance of the ambient intelligence navigation schemes in autonomous mobile robots. Sampling-based path planning algorithms are widely employed in autonomous mobile robot applications. RRT*, or Optimal Rapidly Exploring Random Trees, is a very effective sampling-based path planning algorithm. However, the RRT* solution converges slowly. This study proposes a directional random sampling-based RRT* path planning algorithm known as DR-RRT* to address the slow convergence issue. The novelty of the proposed method is that it reduces the search space by combining directional non-uniform sampling with uniform sampling. It employs a random selection approach to combine the non-uniform directional sampling method with uniform sampling. The proposed path planning algorithm is validated in three different environments with a map size of 384*384, and its performance is compared to two existing algorithms: RRT* and Informed RRT*. Validation is carried out utilizing a TurtleBot3 robot with the Gazebo Simulator and the Robotics Operating System (ROS) Melodic. The proposed DR-RRT* path planning algorithm is better than both RRT* and Informed RRT* in four performance measures: the number of nodes visited, the length of the path, the amount of time it takes, and the rate at which the path converges. The proposed DR-RRT* global path planning algorithm achieves a success rate of 100% in all three environments, and it is suited for use in all kinds of environments.

Ring Attractors as the Basis of a Biomimetic Navigation System.

The ability to navigate effectively in a rich and complex world is crucial for the survival of all animals. Specialist neural structures have evolved that are implicated in facilitating this ability, one such structure being the ring attractor network. In this study, we model a trio of Spiking Neural Network (SNN) ring attractors as part of a bio-inspired navigation system to maintain an internal estimate of planar translation of an artificial agent. This estimate is dynamically calibrated using a memory recall system of landmark-free allotheic multisensory experiences. We demonstrate that the SNN-based ring attractor system can accurately model motion through 2D space by integrating ideothetic velocity information and use recalled allothetic experiences as a positive corrective mechanism. This SNN based navigation system has potential for use in mobile robotics applications where power supply is limited and external sensory information is intermittent or unreliable.

AI-Enabled Condition Monitoring Framework for Indoor Mobile Cleaning Robots

Autonomous mobile cleaning robots are ubiquitous today and have a vast market need. Current studies are mainly focused on autonomous cleaning performances, and there exists a research gap on monitoring the robot’s health and safety. Vibration is a key indicator of system deterioration or external factors causing accelerated degradation or threats. Hence, this work proposes an artificial intelligence (AI)-enabled automated condition monitoring (CM) framework using two heterogeneous sensor datasets to predict the sources of anomalous vibration in mobile robots with high accuracy. This allows triggering proper maintenance or corrective actions based on the condition of the robot’s health or workspace, easing condition-based maintenance (CbM). Anomalous vibration sources are classified as induced by uneven Terrain, Collision with obstacles, loose Assembly, and unbalanced Structure, which causes accelerated system deterioration or potential hazards. Here, an unexplored heterogeneous sensor dataset using inertial measurement unit (IMU) and current sensors is proposed for effective recognition across different vibration classes, resulting in higher-accuracy prediction. A simple-structured 1D convolutional neural network (1D CNN) is developed for training and real-time prediction. A 2D CbM map is generated by fusing the predicted classes in real time on an occupancy grid map of the workspace to monitor the conditions of the robot and workspace remotely. The evaluation test results of the proposed method show that the usage of heterogeneous sensors performs significantly more accurately (98.4%) than previous studies, which used IMU (92.2%) and camera (93.8%) sensors individually. Also, this model is comparatively fast, fit for the environment, and ideal for real-time applications in mobile robots based on field trial validations, enhancing mobile robots’ productivity and operational safety.

A Framework for Optimal Navigation in Situations of Localization Uncertainty.

The basic functions of an autonomous vehicle typically involve navigating from one point to another in the world by following a reference path and analyzing the traversability along this path to avoid potential obstacles. What happens when the vehicle is subject to uncertainties in its localization? All its capabilities, whether path following or obstacle avoidance, are affected by this uncertainty, and stopping the vehicle becomes the safest solution. In this work, we propose a framework that optimally combines path following and obstacle avoidance while keeping these two objectives independent, ensuring that the limitations of one do not affect the other. Absolute localization uncertainty only has an impact on path following, and in no way affects obstacle avoidance, which is performed in the robot's local reference frame. Therefore, it is possible to navigate with or without prior information, without being affected by position uncertainty during obstacle avoidance maneuvers. We conducted tests on an EZ10 shuttle in the PAVIN experimental platform to validate our approach. These experimental results show that our approach achieves satisfactory performance, making it a promising solution for collision-free navigation applications for mobile robots even when localization is not accurate.

Behavior adaptation for mobile robots via semantic map compositions of constraint-based controllers.

Specifying and solving Constraint-based Optimization Problems (COP) has become a mainstream technology for advanced motion control of mobile robots. COP programming still requires expert knowledge to transform specific application context into the right configuration of the COP parameters (i.e., objective functions and constraints). The research contribution of this paper is a methodology to couple the context knowledge of application developers to the robot knowledge of control engineers, which, to our knowledge, has not yet been carried out. The former is offered a selected set of symbolic descriptions of the robots' capabilities (its so-called "behavior semantics") that are translated in control actions via "templates" in a "semantic map"; the latter contains the parameters that cover contextual dependencies in an application and robot vendor-independent way. The translation from semantics to control templates takes place in an "interaction layer" that contains 1) generic knowledge about robot motion capabilities (e.g., depending on the kinematic type of the robots), 2) spatial queries to extract relevant COP parameters from a semantic map (e.g., what is the impact of entering different types of "collision areas"), and 3) generic application knowledge (e.g., how the robots' behavior is impacted by priorities, emergency, safety, and prudence). This particular design of, and interplay between, the application, interaction, and control layers provides a structured, conceptually simple approach to advance the complexity of mobile robot applications. Eventually, industry-wide cooperation between representatives of the application and control communities should result in an interaction layer with different standardized versions of semantic complexity.

Path Planning in Dynamic Environments Based on Q-Learning

With the rapid progress of science and technology, the scope of applications for mobile robots is growing. Path planning in dynamic environment is always a challenging task for mobile robot, which shows significant impacts in the medical field and the military field. Q-learning, a model-free reinforcement learning algorithm, can recognize its surroundings and demonstrate a system making decisions for itself about how this algorithm learns to make accurate decisions about achieving optimal target. Therefore, a path planning algorithm was proposed for robots in dynamic environments based on Q-learning. This algorithm can successfully generate several viable paths in environments with both static obstacles, dynamic obstacles and target point. First, three environments with different levels of complexity were created. Afterwards, to generate several optimal paths, the path planning algorithm was conducted in multiple times in different environments. Finally, the experimental results were collected and visualized to illustrate information. The effectiveness of the proposed algorithm is validated by experiment results in solving problems of path planning in dynamic environments with required point.

SLAMICP Library: Accelerating Obstacle Detection in Mobile Robot Navigation via Outlier Monitoring following ICP Localization.

The Iterative Closest Point (ICP) is a matching technique used to determine the transformation matrix that best minimizes the distance between two point clouds. Although mostly used for 2D and 3D surface reconstruction, this technique is also widely used for mobile robot self-localization by means of matching partial information provided by an onboard LIDAR scanner with a known map of the facility. Once the estimated position of the robot is obtained, the scans gathered by the LIDAR can be analyzed to locate possible obstacles obstructing the planned trajectory of the mobile robot. This work proposes to speed up the obstacle detection process by directly monitoring outliers (discrepant points between the LIDAR scans and the full map) spotted after ICP matching instead of spending time performing an isolated task to re-analyze the LIDAR scans to detect those discrepancies. In this work, a computationally optimized ICP implementation has been adapted to return the list of outliers along with other matching metrics, computed in an optimal way by taking advantage of the parameters already calculated in order to perform the ICP matching. The evaluation of this adapted ICP implementation in a real mobile robot application has shown that the time required to perform self-localization and obstacle detection has been reduced by 36.7% when obstacle detection is performed simultaneously with the ICP matching instead of implementing a redundant procedure for obstacle detection. The adapted ICP implementation is provided in the SLAMICP library.

Reinforcement Learning for Mobile Robotics Exploration: A Survey.

Efficient exploration of unknown environments is a fundamental precondition for modern autonomous mobile robot applications. Aiming to design robust and effective robotic exploration strategies, suitable to complex real-world scenarios, the academic community has increasingly investigated the integration of robotics with reinforcement learning (RL) techniques. This survey provides a comprehensive review of recent research works that use RL to design unknown environment exploration strategies for single and multirobots. The primary purpose of this study is to facilitate future research by compiling and analyzing the current state of works that link these two knowledge domains. This survey summarizes: what are the employed RL algorithms and how they compose the so far proposed mobile robot exploration strategies; how robotic exploration solutions are addressing typical RL problems like the exploration-exploitation dilemma, the curse of dimensionality, reward shaping, and slow learning convergence; and what are the performed experiments and software tools used for learning and testing. Achieved progress is described, and a discussion about remaining limitations and future perspectives is presented.

Adaptive Multi-Robot Communication System and Collision Avoidance Algorithm for Precision Agriculture

In precision farming technology, the interest of the researchers has been focused on the applications of autonomous mobile robots for agricultural operations such as planting, inspection, spraying, and harvesting. However, each autonomous robot generally performs a single agricultural task. In this context, complete autonomy in precision farming can be achieved by using coordinated multi-robot systems that can easily and safely cooperate to accomplish agricultural tasks. The efficiency of the multi-robot system depends on the number of robots, the size of the robots, the distance between each robot, the instant location and heading angle of the robots, and the size of the farmland. This paper describes the development of wireless Robot to Robot (R2R) communication system architecture and the collision avoidance algorithm for multi-robot precision farming applications. The developed system uses the fusion of a digital compass and GPS receiver for wirelessly broadcasting the spatial and temporal data of the mobile robots through WiFi. In this study, WiFi broadcasting was chosen for reasons such as the advantages of long wireless signal range and strength, not being easily affected by weather and dust, low cost, and so on. The proposed system realizes the real-time wireless broadcasting of the mobile robot information for eliminating the collision of mobile robots and improving the level of safety management. The results show that the system has flexible, reliable, and adaptable solution, and thus can increase the efficiency of the multi-robot system in precision farming applications.

Energy Efficient Routing Protocols for Mobile Robots: A Review

Many vital applications benefit from mobile multi-robot teams, including search and rescue, environmental monitoring, military, unmanned space exploration, hazard detection and analysis. Communication between robots is important for group coordination and information sharing in mobile multi-robot teams. Because many mobile robot applications involve scenarios where communication infrastructure is destroyed or unavailable, mobile robot teams must regularly connect with one another through ADHOC networking. In such situations, low over-head and energy efficient routing systems for sending information are essential. The most difficult task is to provide a unified foundation for robot communication. The framework of Mobile Robots is described in this paper, as well as a review and analysis of several research papers on Energy Efficient Routing in Robotic Wireless sensor networks (RWSN).

Safe and Robust Map Updating for Long-Term Operations in Dynamic Environments.

Ensuring safe and continuous autonomous navigation in long-term mobile robot applications is still challenging. To ensure a reliable representation of the current environment without the need for periodic remapping, updating the map is recommended. However, in the case of incorrect robot pose estimation, updating the map can lead to errors that prevent the robot's localisation and jeopardise map accuracy. In this paper, we propose a safe Lidar-based occupancy grid map-updating algorithm for dynamic environments, taking into account uncertainties in the estimation of the robot's pose. The proposed approach allows for robust long-term operations, as it can recover the robot's pose, even when it gets lost, to continue the map update process, providing a coherent map. Moreover, the approach is also robust to temporary changes in the map due to the presence of dynamic obstacles such as humans and other robots. Results highlighting map quality, localisation performance, and pose recovery, both in simulation and experiments, are reported.

Shape Memory Alloy (SMA) Actuators: The Role of Material, Form, and Scaling Effects.

Shape memory alloys (SMAs) are smart materials that are widely used to create intelligent devices because of their high energy density, actuation strain, and biocompatibility characteristics. Given their unique properties, SMAs have been found to have significant potential for implementation in many emerging applications in mobile robots, robotic hands, wearable devices, aerospace/automotive components, and biomedical devices. In this review, we summarize the state-of-the-art of thermal and magnetic SMA actuators in terms of their constituent materials, form, and scaling effects, including their surface treatments and functionalities. We also analyze the motion performance of various SMA architectures (wires, springs, smart soft composites, and knitted/woven actuators). Based on our assessment, we emphasize current challenges of SMAs that need to be addressed for their practical application. Finally, we suggest how to advance SMAs by synergistically considering the effects of material, form, and scale. This article is protected by copyright. All rights reserved.

Safe human-robot collaboration in construction: A conceptual perspective

Introduction: Small mobile robots have become increasingly popular in the construction domain over the last few years. They are stable on rough terrains, can walk over small obstacles, climb stairs, and carry various sensors or arms to perform diverse functions and sub-tasks required to complete construction-related tasks. Saving time, improving accessibility to difficult or unsafe spaces, and reducing costs while accomplishing construction tasks are some of the benefits of using small, mobile robots in construction. However, serious concerns about new workplace hazards could arise from having mobile robots on the jobsite. Unfortunately, no study has attempted to evaluate these risks, especially in the construction domain. Therefore, there was a significant need to develop a holistic understanding of the direct and indirect risks of mobile robot applications in construction. Method: In this paper, we used inferential and Virtual Reality (VR) visualization techniques to: (1) construct conceptual visualizations of proximal and distant human-robot interaction within the construction context; and (2) identify potential safety challenges of robots, which were categorized into three groups: (a) physical risks, (b) attentional costs, and (c) psychological impacts. These identified safety challenges were then validated and ranked by a group of construction safety and robotic experts who had knowledge and experience using such robots in construction. Practical Applications: The outcomes of the study provided a detailed understanding of how robots might adversely affect workers’ safety and health. The study outcomes could also be ultimately used in creating regulatory and administrative guidelines for the safe operations of small mobile robots in construction.

Path planning based on reinforcement learning

With the wide application of mobile robots in industry, path planning has always been a difficult problem for mobile robots. Reinforcement learning algorithms such as Q-learning play a huge role in path planning. Traditional Q-learning algorithm mainly uses - greedy search policy. But for a fixed search factor -greedy. For example, the problems of slow convergence speed, time-consuming and many continuous action transformations (such as the number of turns during robot movement) are not conducive to the stability requirements of mobile robots in industrial transportation. Especially for the transportation of dangerous chemicals, continuous transformation of turns will increase the risk of objects toppling. This paper proposes a new method based on - greedy 's improved dynamic search strategy is used to improve the stability of mobile robots in motion planning. The experiment shows that the dynamic search strategy converges faster, consumes less time, has less continuous transformation times of action, and has higher motion stability in the test environment.

Omnidirectional Continuous Movement Method of Dual-Arm Robot in a Space Station.

The burgeoning complexity of space missions has amplified the research focus on robots that are capable of assisting astronauts in accomplishing tasks within space stations. Nevertheless, these robots grapple with substantial mobility challenges in a weightless environment. This study proposed an omnidirectional continuous movement method for a dual-arm robot, inspired by the movement patterns of astronauts within space stations. On the basis of determining the configuration of the dual-arm robot, the kinematics and dynamics model of the robot during contact and flight phases were established. Thereafter, several constraints are determined, including obstacle constraints, prohibited contact area constraints, and performance constraints. An optimization algorithm based on the artificial bee colony algorithm was proposed to optimize the trunk motion law, contact point positions between the manipulators and the inner wall, as well as the driving torques. Through the real-time control of the two manipulators, the robot is capable of achieving omnidirectional continuous movement across various inner walls with complex structures while maintaining optimal comprehensive performance. Simulation results demonstrate the correctness of this method. The method proposed in this paper provides a theoretical basis for the application of mobile robots within space stations.

Model-Predictive Control for Omnidirectional Mobile Robots in Logistic Environments Based on Object Detection Using CNNs.

Object detection is an essential component of autonomous mobile robotic systems, enabling robots to understand and interact with the environment. Object detection and recognition have made significant progress using convolutional neural networks (CNNs). Widely used in autonomous mobile robot applications, CNNs can quickly identify complicated image patterns, such as objects in a logistic environment. Integration of environment perception algorithms and motion control algorithms is a topic subjected to significant research. On the one hand, this paper presents an object detector to better understand the robot environment and the newly acquired dataset. The model was optimized to run on the mobile platform already on the robot. On the other hand, the paper introduces a model-based predictive controller to guide an omnidirectional robot to a particular position in a logistic environment based on an object map obtained from a custom-trained CNN detector and LIDAR data. Object detection contributes to a safe, optimal, and efficient path for the omnidirectional mobile robot. In a practical scenario, we deploy a custom-trained and optimized CNN model to detect specific objects in the warehouse environment. Then we evaluate, through simulation, a predictive control approach based on the detected objects using CNNs. Results are obtained in object detection using a custom-trained CNN with an in-house acquired data set on a mobile platform and in the optimal control for the omnidirectional mobile robot.

Generative Adversarial Super-Resolution at the edge with knowledge distillation

Single-Image Super-Resolution can support robotic tasks in environments where a reliable visual stream is required to monitor the mission, handle teleoperation or study relevant visual details. In this work, we propose an efficient Generative Adversarial Network model for real-time Super-Resolution, called EdgeSRGAN11Code available at https://github.com/PIC4SeR/EdgeSRGAN.. We adopt a tailored architecture of the original SRGAN and model quantization to boost the execution on CPU and Edge TPU devices, achieving up to 200 fps inference. We further optimize our model by distilling its knowledge to a smaller version of the network and obtain remarkable improvements compared to the standard training approach. Our experiments show that our fast and lightweight model preserves considerably satisfying image quality compared to heavier state-of-the-art models. Finally, we conduct experiments on image transmission with bandwidth degradation to highlight the advantages of the proposed system for mobile robotic applications.

Promoting Transfer of Robot Neuro-Motion-Controllers by Many-Objective Topology and Weight Evolution

The ability of robot motion controllers to quickly adapt to new environments is expected to extend the applications of mobile robots. Using the concept of transfer optimization, this study investigates the capabilities of neuro-motion-controllers, which were obtained by simultaneously solving several source problems, to adapt to target problems. In particular, the adaptation comparison is carried out between specialized controllers, which are optimal for a single source motion problem, and nonspecialized controllers that can solve several source motion problems. The compared types of controllers were simultaneously obtained by a many-objective evolution search that is tailored for the optimization of the topology and weights of neural networks. Based on the examined problems, it appears that nonspecialized solutions, which are “good enough” in all the source motion problems, show significantly better transfer capabilities as compared with solutions that were optimized for a single source motion problem. The proposed approach opens up new opportunities to develop controllers that have good enough performances in various environments while also exhibiting efficient adaptation capabilities to changes in the environments.