Mobile Robot Research Articles

AN IMPROVED COVERAGE PATH PLANNING FOR SERVICE ROBOTS BASED ON BACKTRACKING METHOD

Service robots are of paramount importance in both industrial and residential environments. Nevertheless, these robots frequently encounter obstacles in the form of perilous elements present in their surroundings, which may result in their malfunction and hinder their optimal performance. Hence, autonomous robot applications heavily rely on coverage path planning algorithms, which facilitate the execution of area coverage operations in a streamlined and economical manner. The article presents a coverage path planning algorithm for service robots that is based on a backtracking procedure. The proposed method attempts to strike a balance between the safety of the cleaning automaton and the extent of coverage. Following straight-forward instructions, the mobile robot (MR) initially generates a spiral or zigzag trajectory. It examines each position for possible backtracking locations. When obstacles and visited positions obstruct the path, the improved A* algorithm is utilized to return to known unvisited regions. Subsequently, the robot proceeds to sweep the regions until no remaining back-tracking sites remain. Simulation of the method on the Robot Operating System (ROS) verifies that it is possible to restrict the movement of high-risk robots.

DMPC-based control solution for mobile robots platoon based on ZigBee communication

The integration of mobile robots into various applications, operating collaboratively in groups known as platoons, presents challenges in communication, control, and synchronization. This paper introduces a control solution for mobile robot platoons based on a DMPC algorithm and the ZigBee protocol for V2V communication. This communication standard was selected for its low power consumption, cost-effectiveness, and reliable data transmission over short distances. Furthermore, a testbed and methodology for the experimental evaluation of the proposed control solution were developed. The results demonstrate the efficacy and feasibility of the proposed control and communication solutions.

Intelligent mobile robot navigation in unknown and complex environment using reinforcement learning technique

The usage of mobile robots (MRs) has expanded dramatically in the last several years across a wide range of industries, including manufacturing, surveillance, healthcare, and warehouse automation. To ensure the efficient and safe operation of these MRs, it is crucial to design effective control strategies that can adapt to changing environments. In this paper, we propose a new technique for controlling MRs using reinforcement learning (RL). Our approach involves mathematical model generation and later training a neural network (NN) to learn a policy for robot control using RL. The policy is learned through trial and error, where MR explores the environment and receives rewards based on its actions. The rewards are designed to encourage the robot to move towards its goal while avoiding obstacles. In this work, a deep Q-learning (QL) agent is used to enable robots to autonomously learn to avoid collisions with obstacles and enhance navigation abilities in an unknown environment. When operating MR independently within an unfamiliar area, a RL model is used to identify the targeted location, and the Deep Q-Network (DQN) is used to navigate to the goal location. We evaluate our approach using a simulation using the Epsilon-Greedy algorithm. The results show that our approach outperforms traditional MR control strategies in terms of both efficiency and safety.

A radioactive source-seeking method based on angle constraint and particle diffusion

The deployment of mobile robots to conduct search for radioactive sources plays a crucial role in preventing radiation pollution and safeguarding public health. And it is a significant challenge to accurately locate radioactive sources in unknown environments. Herein, a particle filter based on mobile robot search method is proposed to localize radiation sources. By applying an angle constraint technique, the complexity of this algorithm is reduced, the high accuracy of source estimation is maintained, and the range of search is expanded. Additionally, a particle diffusion technology is applied to address particle loss that occurs during filtering. Furthermore, an adaptive gradient descent strategy is adopted to enhance the search efficiency. Experimental results demonstrate that this method achieves a success rate of over 95 % within a rectangular area (e.g., 40m by 40m), outperforming traditional methods, and it could perform effectively in dual radioactive source search tasks.

The use of digital twin for mobile robot swarm task allocation

Cyber-physical systems, with the advent of the Internet of Things (IoT), have paved the way for the rise of the Industrial Internet of Things (IIoT) in industrial environments. A significant manifestation of this synergy is the Digital Twin—a virtual representation of industrial entities, including robots. This paper examines the fusion of digital twins in swarm robotics, emphasizing communication, and task allocation among robots. This study employs several mobile robots interconnected via a wireless network, each equipped with a Raspberry Pi and specific sensors. A centralized server, acting as both a Docker host and a Docker registry, facilitates task allocation. The results indicate effective communication among robots, with the digital twin playing an instrumental role in safety assessment and task allocation, as well as deploying Docker containers using Portainer and GitLab APIs. Unity3D aids in visualizing IMU motion and orientation. The motivation behind this study stems from the need to enhance efficiency and safety in industrial settings through advanced automation and coordination in swarm robotics. This research not only demonstrates the feasibility of integrating digital twins with IoT-enabled robots but also sets the stage for future investigations into more complex task allocation and safety protocols in industrial environments. By leveraging digital twins in swarm robotics, we open new pathways for research into more adaptive, responsive, and efficient industrial systems.

Parking Problem by Oblivious Mobile Robots in Infinite Grids

In this paper, the parking problem of a swarm of mobile robots has been studied. The robots are deployed at the nodes of an infinite grid, which has a subset of prefixed nodes marked as parking nodes. A parking node pi has a capacity of ki, which is given as an input and represents the number of robots it is capable of accommodating. As a solution to the parking problem, in the final configuration, robots need to partition themselves into groups so that each parking node contains a number of robots that are equal to the capacity of the node. It is assumed that the number of robots in the initial configuration equals the sum of the capacities of the parking nodes. The robots are assumed to be autonomous, anonymous, homogeneous, identical and oblivious. They operate under an asynchronous scheduler. They do not have any agreement on the coordinate axes, nor do they agree on a common chirality. All the initial configurations for which the problem is unsolvable have been identified. A deterministic distributed algorithm has been proposed for the remaining configurations, ensuring the solvability of the problem.

Management of experimental workflows in robotic cultivation platforms

In the last decades, robotic cultivation facilities combined with automated execution of workflows have drastically increased the speed of research in biotechnology. In this work, we present the design and deployment of a digital infrastructure for robotic cultivation platforms. We implement a workflow management system, using Directed Acyclic Graphs, based on the open-source platform Apache Airflow to increase traceability and the automated execution of experiments. We demonstrate the integration and automation of experimental workflows in a laboratory environment with a heterogeneous device landscape including liquid handling stations, parallel cultivation systems, and mobile robots. The feasibility of our approach is assessed in parallel E. coli fed-batch cultivations with glucose oscillations in which different elastin-like proteins are produced. We show that the use of workflow management systems in robotic cultivation platforms increases automation, robustness and traceability of experimental data.

Design and prototyping of smart mobile grinding bots as an educational experience

Abrasive finishing of large areas is often done manually and can be time-consuming, ergonomically challenging, and even detrimental to the operator’s health. Inspired by ant robots, this paper presents a senior design project that developed a grinding robot and a quality robot, which can grind and assess a sheet metal surface. This concept of mobile grinding robots is novel and demands a multidisciplinary design approach. The main requirements included applying a controlled pressure with the grinding spindle to the surface, having a range of mobility within a predefined work area, and communication between the robots. A first prototype was delivered based on Linkbot modules, a commercial power tool as grinding spindle, sensors to measure spindle height, taped work area boundary, and surface finish. The mechanical and electronical system performance was analyzed and tested. This paper presents a proof of concept for smart mobile grinding bots and a reproducible educational experience.

Chinese Bayberry Detection in an Orchard Environment Based on an Improved YOLOv7-Tiny Model

The precise detection of Chinese bayberry locations using object detection technology is a crucial step to achieve unmanned harvesting of these berries. Because of the small size and easy occlusion of bayberry fruit, the existing detection algorithms have low recognition accuracy for such objects. In order to realize the fast and accurate recognition of bayberry in fruit trees, and then guide the robotic arm to carry out accurate fruit harvesting, this paper proposes a detection algorithm based on an improved YOLOv7-tiny model. The model introduces partial convolution (PConv), a SimAM attention mechanism and SIoU into YOLOv7-tiny, which enables the model to improve the feature extraction capability of the target without adding extra parameters. Experimental results on a self-built Chinese bayberry dataset demonstrate that the improved algorithm achieved a recall rate of 97.6% and a model size of only 9.0 MB. Meanwhile, the precision of the improved model is 88.1%, which is 26%, 2.7%, 4.7%, 6.5%, and 4.7% higher than that of Faster R-CNN, YOLOv3-tiny, YOLOv5-m, YOLOv6-n, and YOLOv7-tiny, respectively. In addition, the proposed model was tested under natural conditions with the five models mentioned above, and the results showed that the proposed model can more effectively reduce the rates of misdetections and omissions in bayberry recognition. Finally, the improved algorithm was deployed on a mobile harvesting robot for field harvesting experiments, and the practicability of the algorithm was further verified.

Mixed obstacle avoidance in mobile chaotic robots with directional keypads and its non-identical generalized synchronization

Mixed obstacle avoidance in mobile chaotic robots with directional keypads and its non-identical generalized synchronization

Hopping potential wells and gait switching in a fish-like robot with a bistable tail

Fish outperform current underwater robots in speed, agility, and efficiency of locomotion, in part due to their flexible appendages that are capable of rich combinations of modes of motion. In fish-like robots, actuating many different modes of oscillation of tails or fins can become a challenge. This paper presents a highly underactuated (with a single actuator) fish-like robot with a bistable tail that features a double-well elastic potential. Oscillations of such a tail depend on the frequency and amplitude of excitation, and tuning the frequency-amplitude can produce controllable oscillations in different modes leading to different gaits of the robot. This robot design is inspired by recent work on underactuated flexible swimming robots driven by a single rotor. The oscillations of the rotor can propel and steer the robot, but saturation of the rotor makes performing long turns challenging. This paper demonstrates that by adding geometric bistability to the flexible tail, turns can be performed by controllably exciting single-well oscillations in the tail, while exciting double-well oscillations of the tail produces average straight-line motion. The findings of this paper go beyond the application to a narrow class of fish-like robots. More broadly we have demonstrated the use of periodic excitation to produce bistable response that generate different gaits including a steering gait. The mechanics demonstrated here show the feasibility of applications to other mobile soft robots.

Controlling mobile robot in flat environment taking into account nonlinear factors applying artificial intelligence

Controlling mobile robot in flat environment taking into account nonlinear factors applying artificial intelligence

Adaptive hierarchical energy management strategy for fuel cell mobile robot hybrid power system based on working condition recognition

Energy management of hybrid power is critical to maintain the economical and efficient operation of fuel cell mobile robots. To improve the energy distribution between the proton exchange membrane fuel cell (PEMFC) and battery under different working conditions, this paper proposes an adaptive hierarchical energy management strategy (AHEMS) based on the recognition and management levels. Firstly, the recognition level realizes the identification of different working conditions based on the machine learning (ML) methods including the K-means and KNN. Secondly, the fuel cell hydrogen consumption and efficiency are both optimized by adaptive multi-objective particle swarm optimization (AMOPSO) at the management level. Specifically, an adaptive flight parameter strategy based on the particle dispersity (PD) information is proposed to balance the convergence and diversity of Pareto solutions. Besides, to overcome the parameter uncertainty caused by different working states and improve the system performance, an interval optimization scheme is proposed based on the Pareto solutions. Finally, the fuzzy decision combined with the recognition results and state of charge (SOC) of the battery is performed to find the most appropriate power distribution of the PEMFC and battery. The proposed AHEMS algorithm is compared with different algorithms in the numerical simulation and hardware-in-loop (HIL) experiments. These results demonstrate that the hybrid power system with the proposed optimization scheme performs better than the base model and classical optimization algorithms in terms of the hydrogen consumption and efficiency indexes, revealing the success of this AHEMS approach in solving the energy distribution problem in different working conditions.

Efficient Online Planning and Robust Optimal Control for Nonholonomic Mobile Robot in Unstructured Environments

Efficient Online Planning and Robust Optimal Control for Nonholonomic Mobile Robot in Unstructured Environments

Collision-Free Tracking for a Mobile Robot Based on Purely Geometric Planning

A purely geometric planning method for a mobile robot in unknown environments is proposed to ensure robot collision avoidance against obstacles within the safety time interval while moving to the goal. The robot initially detects a point cloud of obstacles using a 2D LiDAR. Euclidean clustering is employed to classify the point cloud into point classes. The obstacle is then identified as a directed closed-loop rectangle for each point class. A relative orientation kd-tree is designed to store the vertices of the obstacles and to determine the obstacles to be considered in the obstacle avoidance algorithm. A velocity divider is introduced to obtain a linear convex area of possible obstacle avoidance velocities. Thus, linear planning is employed to calculate the optimal obstacle avoidance velocity for control. A virtual reference point method is proposed to solve the problem of an unreachable goal in a singular configuration. Experimental results show that the directed closed-loop rectangle and the relative orientation kd-tree facilitate the quick updating of required obstacle points with low-cost sensor equipment. The deterministic path for a given scenario can be obtained, demonstrating the reliability of geometric planning for both the obstacle avoidance velocity region and the optimal obstacle avoidance velocity. The proposed algorithm is finally validated for multiple obstacles and dynamic environments with moving obstacles.

Multi-agent long-distance end-to-end indoor navigation: using imitation learning pre-training and global map

Abstract To address the navigation challenges of mobile robot swarm coordination in complex environments, this paper presents a distributed navigation approach for mobile robot swarm. The study initially formulates the problem of robot swarm navigation as a partially observable Markov decision process, employing a distributed proximal policy optimization algorithm to train decision models mapping observations to actions directly. Furthermore, it adopts imitation learning pre-training to maximize expert decisions in the initial training phase, thereby reducing exploration space. Lastly, an improved path point generation algorithm along with a spatiotemporal reachability calculation method is proposed to guide the Deep Reinforcement Learning policy in accomplishing long-distance indoor navigation. Comprehensive performance evaluations are conducted in simulated environments to compare the proposed method with existing approaches. Results demonstrate that the proposed approach effectively enhances both convergence speed and model performance, while significantly improving the navigation capabilities of robot swarms in complex environments.

Real-Time Submap Trimming-Based Map Updating for Long-Term Localization of Mobile Robot in Dynamic Environments

Real-Time Submap Trimming-Based Map Updating for Long-Term Localization of Mobile Robot in Dynamic Environments

Enclose and Track a Target of Mobile Robot With Motion and Field of View Constraints Based on Relative Position Measurement

Enclose and Track a Target of Mobile Robot With Motion and Field of View Constraints Based on Relative Position Measurement

Bi-Directional Vibration-Driven Mobile Robots Based on Multipole Magnetoactive Elastomers

Bi-Directional Vibration-Driven Mobile Robots Based on Multipole Magnetoactive Elastomers

Safe Motion Planning and Control for Mobile Robots: A Survey

Safe Motion Planning and Control for Mobile Robots: A Survey