Artificial Pheromone System Using RFID for Navigation of Autonomous Robots

Navigation system based on the animal behavior has received a growing attention in the past few years. The navigation systems using artificial pheromone are still few so far. For this reason, this paper presents our research that aim to implement autonomous navigation with artificial pheromone system. By introducing artificial pheromone system composed of data carriers and autonomous robots, the robotic system creates a potential field to navigate their group. We have developed a pheromone density model to realize the function of pheromones with the help of data carders. We intend to show the effectiveness of the proposed system by performing simulations and realization using modified mobile robot. The pheromone potential field system can be used for navigation of autonomous robots.

A New Generation of Rules-based Approach:Mivar-based Intelligent Planning of Robot Actions(MIPRA)and Brains for Autonomous Robots

To create autonomous robots,both hardware and software are needed.If enormous progress has already been made in the field of equipment,then robot software depends on the development of artificial intelligence.This article proposes a solution for creating“logical”brains for autonomous robots,namely,an approach for creating an intelligent robot action planner based on Mivar expert systems.The application of this approach provides opportunities to reduce the computational complexity of solving planning problems and the requirements for the computational characteristics of hardware platforms on which intelligent planning systems are deployed.To theoretically and practically justify the expediency of using logically solving systems,in particular Mivar expert systems,to create intelligent planners,the MIPRA(Mivar-based Intelligent Planning of Robot Actions)planner was created to solve problems such as STRIPS for permutation cubes in the Blocks World domain.The planner is based on the platform for creating expert systems of the Razumator.As a result,the Mivar planner can process information about the state of the subject area based on the analysis of cause-effect relationships and an algorithm for automatically constructing logical inference(finding a solution from“Given”to“Find”).Moreover,an important feature of the MIPRA is that the system is built on the principles of a“white box”,due to which the system can explain any of its decisions and provide justification for the actions performed in the form of a retrospective of the stages of the decision-making process.When preparing a set of robot actions aimed at changing control objects,expert knowledge is used,which is the basis for the functioning algorithms of the planner.This approach makes it possible to include an expert in the process of organizing the work of the intelligent planner and use existing knowledge about the subject area.Practical experiments of this study have shown that instead of many hours and powerful multiprocessor servers,the MIPRA on a personal computer solves the planning problems with the following number of cubes:10 cubes can be rearranged in 0.028 seconds,100 cubes in 0.938 seconds,and 1000 cubes in 84.188 seconds.The results of this study can be used to reduce the computational complexity of solving tasks of planning the actions of robots,as well as their groups,multilevel heterogeneous robotic systems,and cyber-physical systems of various bases and purposes.

AI Safety Approach for Minimizing Collisions in Autonomous Navigation

Autonomous agents can explore the environment around them when equipped with advanced hardware and software systems that help intelligent agents minimize collisions.These systems are developed under the term Artificial Intelligence(AI)safety.AI safety is essential to provide reliable service to consumers in various fields such asmilitary,education,healthcare,and automotive.This paper presents the design of an AI safety algorithmfor safe autonomous navigation using Reinforcement Learning(RL).Machine Learning Agents Toolkit(ML-Agents)was used to train the agentwith a proximal policy optimizer algorithmwith an intrinsic curiositymodule(PPO+ICM).This training aims to improve AI safety and minimize or prevent any mistakes that can cause dangerous collisions by the intelligent agent.Four experiments have been executed to validate the results of our research.The designed algorithmwas tested in a virtual environment with four differentmodels.A comparison was presented in four cases to identify the best-performing model for improvingAI safety.The designed algorithmenabled the intelligent agent to perform the required task safely using RL.A goal collision ratio of 64%was achieved,and the collision incidents were minimized from 134 to 52 in the virtual environment within 30min.

Cognitive Supervisor for an Autonomous Swarm of Robots

As a sequel to our recent work [1], in which a control framework was developed for large-scale joint swarms of unmanned ground (UGV) and aerial (UAV) vehicles, the present paper proposes cognitive and meta-cognitive supervisor models for this kind of distributed robotic system. The cognitive supervisor model is a formalization of the recently Nobel-awarded research in brain science on mammalian and human path integration and navigation, performed by the hippocampus. This is formalized here as an adaptive Hamiltonian path integral, and efficiently simulated for implementation on robotic vehicles as a pair of coupled nonlinear Schr?dinger equations. The meta-cognitive supervisor model is a modal logic of actions and plans that hinges on a weak causality relation that specifies when atoms may change their values without specifying that they must change. This relatively simple logic is decidable yet sufficiently expressive to support the level of inference needed in our application. The atoms and action primitives of the logic framework also provide a straight-forward way of connecting the meta-cognitive supervisor with the cognitive supervisor, with other modules, and to the meta-cognitive supervisors of other robotic platforms in the swarm.

Concept of Intelligent Mechanical Design for Autonomous Mobile Robots

The concept of Intelligent Mechanical Design （IMD） is presented to show how a mechanical structure can be designed to affect robot controllability, simplification and task performance. Exploring this concept produces landmarks in the territory of mechanical robot design in the form of seven design principles. The design principles, which we call the Mecha-Telligence Principles （MTP）, provide guidance on how to design mechanics for autonomous mobile robots. These principles guide us to ask the right questions when investigating issues concerning self-controllable, reliable, feasible, and compatible mechanics for autonomous mobile robots. To show how MTP can be applied in the design process we propose a novel methodology, named as Mecha-Telligence Methodology （MTM）. Mechanical design by the proposed methodology is based on preference classification of the robot specification described by interaction of the robot with its environment and the physical parameters of the robot mechatronics. After defining new terms, we investigate the feasibility of the proposed methodology to the mechanical design of an autonomous mobile sewer inspection robot. In this industrial project we show how a passive-active intelligent moving mechanism can be designed using the MTM and employed in the field.

CBF-Based Distributed Model Predictive Control for Safe Formation of Autonomous Mobile Robots

A distributed model predictive control(DMPC)method based on robust control barrier function(RCBF)is developed to achieve the safe formation target of multi-autonomous mobile robot systems in an uncertain disturbed environment.The first step is to analyze the safety requirements of the system during safe formation and categorize them into collision avoidance and distance connectivity maintenance.RCBF constraints are designed based on collision avoidance and connectivity maintenance requirements,and security constraints are achieved through a combination.Then,the specified safety constraints are integrated with the objective of forming a multi-autonomous mobile robot formation.To ensure safe control,the optimization problem is integrated with the DMPC method.Finally,the RCBF-DMPC algorithm is proposed to ensure iterative feasibility and stability while meeting the constraints and expected objectives.Simulation experiments illustrate that the designed algorithm can achieve cooperative formation and ensure system security.

Location estimation of autonomous driving robot and 3D tunnel mapping in underground mines using pattern matched LiDAR sequential images

In this study,a machine vision-based pattern matching technique was applied to estimate the location of an autonomous driving robot and perform 3D tunnel mapping in an underground mine environment.The autonomous driving robot continuously detects the wall of the tunnel in the horizontal direction using the light detection and ranging(Li DAR)sensor and performs pattern matching by recognizing the shape of the tunnel wall.The proposed method was designed to measure the heading of the robot by fusion with the inertial measurement units sensor according to the pattern matching accuracy;it is combined with the encoder sensor to estimate the location of the robot.In addition,when the robot is driving,the vertical direction of the underground mine is scanned through the vertical Li DAR sensor and stacked to create a 3D map of the underground mine.The performance of the proposed method was superior to that of previous studies;the mean absolute error achieved was 0.08 m for the X-Y axes.A root mean square error of 0.05 m^(2)was achieved by comparing the tunnel section maps that were created by the autonomous driving robot to those of manual surveying.

Intelligent Autonomous-Robot Control for Medical Applications

The COVID-19 pandemic has shown that there is a lack of healthcare facilities to cope with a pandemic.This has also underscored the immediate need to rapidly develop hospitals capable of dealing with infectious patients and to rapidly change in supply lines to manufacture the prescription goods(including medicines)that is needed to prevent infection and treatment for infected patients.The COVID-19 has shown the utility of intelligent autonomous robots that assist human efforts to combat a pandemic.The artificial intelligence based on neural networks and deep learning can help to fight COVID-19 in many ways,particularly in the control of autonomous medic robots.Health officials aim to curb the spread of COVID-19 among medical,nursing staff and patients by using intelligent robots.We propose an advanced controller for a service robot to be used in hospitals.This type of robot is deployed to deliver food and dispense medications to individual patients.An autonomous line-follower robot that can sense and follow a line drawn on the floor and drive through the rooms of patients with control of its direction.These criteria were met by using two controllers simultaneously:a deep neural network controller to predict the trajectory of movement and a proportional-integral-derivative(PID)controller for automatic steering and speed control.

Tour Planning Design for Mobile Robots Using Pruned Adaptive Resonance Theory Networks

The development of intelligent algorithms for controlling autonomous mobile robots in real-time activities has increased dramatically in recent years.However,conventional intelligent algorithms currently fail to accurately predict unexpected obstacles involved in tour paths and thereby suffer from inefficient tour trajectories.The present study addresses these issues by proposing a potential field integrated pruned adaptive resonance theory(PPART)neural network for effectively managing the touring process of autonomous mobile robots in real-time.The proposed system is implemented using the AlphaBot platform,and the performance of the system is evaluated according to the obstacle prediction accuracy,path detection accuracy,time-lapse,tour length,and the overall accuracy of the system.The proposed system provide a very high obstacle prediction accuracy of 99.61%.Accordingly,the proposed tour planning design effectively predicts unexpected obstacles in the environment and thereby increases the overall efficiency of tour navigation.

The Intellectualized Architecture of the Autonomous Micro- Mobile Robot Based- Behavior

Given the difficulty in hand coding task schemes, an intellectualized architecture of the autonomous micro mobile robot based behavior for fault repair was presented. Integrating the reinforcement learning and the group behavior evolution simulating the human's learning and evolution, the autonomous micro mobile robot will automatically generate the suited actions satisfied the environment. However, the designer only devises some basic behaviors, which decreases the workload of the designer and cognitive deficiency of the robot to the environment. The results of simulation have shown that the architecture endows micro robot with the ability of learning, adaptation and robustness, also with the ability of accomplishing the given task.

Megabots Team： A Team for the Category Sumo Autonomous 3 kg

This paper deals with a method for building a mobile robot in order to transform the material into a practical guide for beginners in the study of mobile robotics. The project is divided into layers that can define the topics related to the areas of knowledge that will be used in carrying out the project. These areas are the mechanics, electronics and computing system. The mobile robot named Fable was developed accordingly to this method. It is composed by two active wheels, each one driven by DC motor with a high torque and a transmission system containing two spur gears. It has three sonars for detection of the opponent and two infrared sensors to detect a line and an Arduino Uno board is used to control all the actions of the robot.

Business Plan for Autonomous Delivery Robot

This paper introduces an autonomous robot (AR) cart to execute the last mile delivery task. We use navigation and intelligent avoidance algorithms to plan the path of the automatic robot. When AR encounters a new unrecognizable terrain, it will give control to the customer who can control the AR on its mobile app and navigate to the specified destination. We have initially designed an autonomous delivery robot with the cost of 2774 dollars.

Mobile Robot Indoor Autonomous Navigation with Position Estimation Using RF Signal Triangulation

In the mobile robotic systems a precise estimate of the robot pose (Cartesian [x y] position plus orientation angle theta) with the intention of the path planning optimization is essential for the correct performance, on the part of the robots, for tasks that are destined to it, especially when intention is for mobile robot autonomous navigation. This work uses a ToF (Time-of-Flight) of the RF digital signal interacting with beacons for computational triangulation in the way to provide a pose estimative at bi-dimensional indoor environment, where GPS system is out of range. It’s a new technology utilization making good use of old ultrasonic ToF methodology that takes advantage of high performance multicore DSP processors to calculate ToF of the order about ns. Sensors data like odometry, compass and the result of triangulation Cartesian estimative, are fused in a Kalman filter in the way to perform optimal estimation and correct robot pose. A mobile robot platform with differential drive and nonholonomic constraints is used as base for state space, plants and measurements models that are used in the simulations and for validation the experiments.

Glass Recognition and Map Optimization Method for Mobile Robot Based on Boundary Guidance

Current research on autonomous mobile robots focuses primarily on perceptual accuracy and autonomous performance.In commercial and domestic constructions,concrete,wood,and glass are typically used.Laser and visual mapping or planning algorithms are highly accurate in mapping wood panels and concrete walls.However,indoor and outdoor glass curtain walls may fail to perceive these transparent materials.In this study,a novel indoor glass recognition and map optimization method based on boundary guidance is proposed.First,the status of glass recognition techniques is analyzed comprehensively.Next,a glass image segmentation network based on boundary data guidance and the optimization of a planning map based on depth repair are proposed.Finally,map optimization and path-planning tests are conducted and compared using different algorithms.The results confirm the favorable adaptability of the proposed method to indoor transparent plates and glass curtain walls.Using the proposed method,the recognition accuracy of a public test set increases to 94.1%.After adding the planning map,incorrect coverage redundancies for two test scenes reduce by 59.84%and 55.7%.Herein,a glass recognition and map optimization method is proposed that offers sufficient capacity in perceiving indoor glass materials and recognizing indoor no-entry regions.

An Incremental Time-delay Neural Network for Dynamical Recurrent Associative Memory

An incremental time-delay neural network based on synapse growth, which is suitable for dynamic control and learning of autonomous robots, is proposed to improve the learning and retrieving performance of dynamical recurrent associative memory architecture. The model allows steady and continuous establishment of associative memory for spatio-temporal regularities and time series in discrete sequence of inputs. The inserted hidden units can be taken as the long-term memories that expand the capacity of network and sometimes may fade away under certain condition. Preliminary experiment has shown that this incremental network may be a promising approach to endow autonomous robots with the ability of adapting to new data without destroying the learned patterns. The system also benefits from its potential chaos character for emergence.

Digital twins in smart farming:An autoware-based simulator for autonomous agricultural vehicles

Digital twins can improve the level of control over physical entities and help manage complex systems by integrating a range of technologies.The autonomous agricultural machine has shown revolutionary effects on labor reduction and utilization rate in field works.Autonomous vehicles in precision agriculture have the potential to improve competitiveness compared to current crop production methods and have become a research hotspot.However,the development time and resources required in experiments have limited the research in this area.Simulation tools in unmanned farming that are required to enable more efficient,reliable,and safe autonomy are increasingly demanding.Inspired by the recent development of an open-source virtual simulation platform,this study proposed an autoware-based simulator to evaluate the performance of agricultural machine guidance based on digital twins.Oblique photogrammetry using drones is used to construct threedimensional maps of fields at the same scale as reality.A communication format suitable for agricultural machines was developed for data input and output,along with an inter-node communication methodology.The conversion,publishing,and maintenance of multiple coordinate systems were completed based on ROS(Robot Operating System).Coverage path planning was performed using hybrid curves based on Bézier curves,and it was tested in both a simulation environment and actual fields with the aid of Pure Pursuit algorithms and PID controllers.

Analytical modeling and multi-objective optimization(MOO) of slippage for wheeled mobile robot(WMR) in rough terrain

Good understanding of relationship between parameters of vehicle, terrain and interaction at the interface is required to develop effective navigation and motion control algorithms for autonomous wheeled mobile robots （AWMR） in rough terrain. A model and analysis of relationship among wheel slippage （S）, rotation angle （0）, sinkage （z） and wheel radius （r） are presented. It is found that wheel rotation angle, sinkage and radius have some influence on wheel slippage. A multi-objective optimization problem with slippage as utility function was formulated and solved in MATLAB. The results reveal the optimal values of wheel-terrain parameters required to achieve optimum slippage on dry sandy terrain. A method of slippage estimation for a five-wheeled mobile robot was presented through comparing the odometric measurements of the powered wheels with those of the fifth non-powered wheel. The experimental result shows that this method is feasible and can be used for online slippage estimation in a sandy terrain.

State-chain sequential feedback reinforcement learning for path planning of autonomous mobile robots

This paper deals with a new approach based on Q-learning for solving the problem of mobile robot path planning in complex unknown static environments.As a computational approach to learning through interaction with the environment,reinforcement learning algorithms have been widely used for intelligent robot control,especially in the field of autonomous mobile robots.However,the learning process is slow and cumbersome.For practical applications,rapid rates of convergence are required.Aiming at the problem of slow convergence and long learning time for Q-learning based mobile robot path planning,a state-chain sequential feedback Q-learning algorithm is proposed for quickly searching for the optimal path of mobile robots in complex unknown static environments.The state chain is built during the searching process.After one action is chosen and the reward is received,the Q-values of the state-action pairs on the previously built state chain are sequentially updated with one-step Q-learning.With the increasing number of Q-values updated after one action,the number of actual steps for convergence decreases and thus,the learning time decreases,where a step is a state transition.Extensive simulations validate the efficiency of the newly proposed approach for mobile robot path planning in complex environments.The results show that the new approach has a high convergence speed and that the robot can find the collision-free optimal path in complex unknown static environments with much shorter time,compared with the one-step Q-learning algorithm and the Q(λ)-learning algorithm.

Active Tracking with Pan-Tilt-Zoom Camera

This paper intends to introduce the active vision system with autonomous robot competition in the background. It presents some characteristics of the human oculomotor system in active ma-chine vision svstem: smooth pursuit and saccade. Besides, zoom tracking is used to continuous ad-justment of a camera's focal length to keep a constant sized image of an object moving along the camera's optical axis. Experiments indicate the technology to be efficient for tracking the bail in the robot competition.

Autonomous mobile robot global path planning: a prior information-based particle swarm optimization approach

The path planning of autonomous mobile robots(PPoAMR)is a very complex multi-constraint problem.The main goal is to find the shortest collision-free path from the starting point to the target point.By the fact that the PPoAMR problem has the prior knowledge that the straight path between the starting point and the target point is the optimum solution when obstacles are not considered.This paper proposes a new path planning algorithm based on the prior knowledge of PPoAMR,which includes the fitness value calculation method and the prior knowledge particle swarm optimization(PKPSO)algorithm.The new fitness calculation method can preserve the information carried by each individual as much as possible by adding an adaptive coefficient.The PKPSO algorithm modifies the particle velocity update method by adding a prior particle calculated from the prior knowledge of PPoAMR and also implemented an elite retention strategy,which improves the local optima evasion capability.In addition,the quintic polynomial trajectory optimization approach is devised to generate a smooth path.Finally,some experimental comparisons with those state-of-the-arts are carried out to demonstrate the effectiveness of the proposed path planning algorithm.