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.

Preliminary laboratory test on navigation accuracy of an autonomous robot for measuring air quality in livestock buildings

Air quality in many poultry buildings is less than desirable.However,the measurement of concentrations of airborne pollutants in livestock buildings is generally quite difficult.To counter this,the development of an autonomous robot that could collect key environmental data continuously in livestock buildings was initiated.This research presents a specific part of the larger study that focused on the preliminary laboratory test for evaluating the navigation precision of the robot being developed under the different ground surface conditions and different localization algorithm according internal sensors.The construction of the robot was such that each wheel of the robot was driven by an independent DC motor with four odometers fixed on each motor.The inertial measurement unit(IMU)was rigidly fixed on the robot vehicle platform.The research focused on using the internal sensors to calculate the robot position(x,y,θ)through three different methods.The first method relied only on odometer dead reckoning(ODR),the second method was the combination of odometer and gyroscope data dead reckoning(OGDR)and the last method was based on Kalman filter data fusion algorithm(KFDF).A series of tests were completed to generate the robot’s trajectory and analyse the localisation accuracy.These tests were conducted on different types of surfaces and path profiles.The results proved that the ODR calculation of the position of the robot is inaccurate due to the cumulative errors and the large deviation of the heading angle estimate.However,improved use of the gyroscope data of the IMU sensor improved the accuracy of the robot heading angle estimate.The KFDF calculation resulted in a better heading angle estimate than the ODR or OGDR calculations.The ground type was also found to be an influencing factor of localisation errors.

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.

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.

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.

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.

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.

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.

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.

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.

Robot navigation system using intrinsic evolvable hardware

Recently there has been great interest in the idea that evolvable system based on the principle of artifcial intelligence can be used to continuously and autonomously adapt the behaviour of physically embedded systems such as autonomous mobile robots and intelligent home devices. Meanwhile, we have seen the introduction of evolvable hardware(EHW): new integrated electronic circuits that are able to continuously evolve to adapt the chages in the environment implemented by evolutionary algorithms such as genetic algorithm(GA) and reinforcement learning. This paper concentrates on developing a robotic navigation system whose basic behaviours are obstacle avoidance and light source navigation. The results demonstrate that the intrinsic evolvable hardware system is able to create the stable robotiiuc behaviours as required in the real world instead of the traditional hardware systems.

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.

Graph-based robot optimal path planning with bio-inspired algorithms

Recently,bio-inspired algorithms have been increasingly explored for autonomous robot path planning on grid-based maps.However,these approaches endure performance degradation as problem complexity increases,often resulting in lengthy search times to find an optimal solution.This limitation is particularly critical for real-world applications like autonomous off-road vehicles,where highquality path computation is essential for energy efficiency.To address these challenges,this paper proposes a new graph-based optimal path planning approach that leverages a sort of bio-inspired algorithm,improved seagull optimization algorithm(iSOA)for rapid path planning of autonomous robots.A modified Douglas–Peucker(mDP)algorithm is developed to approximate irregular obstacles as polygonal obstacles based on the environment image in rough terrains.The resulting mDPderived graph is then modeled using a Maklink graph theory.By applying the iSOA approach,the trajectory of an autonomous robot in the workspace is optimized.Additionally,a Bezier-curve-based smoothing approach is developed to generate safer and smoother trajectories while adhering to curvature constraints.The proposed model is validated through simulated experiments undertaken in various real-world settings,and its performance is compared with state-of-the-art algorithms.The experimental results demonstrate that the proposed model outperforms existing approaches in terms of time cost and path length.

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.

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.

Real-Time Fuzzy Obstacle Avoidance Using Directional Visual Perception

This paper presents a novel vision-based obstacle avoidance approach for the Autonomous Mobile Robot (AMR) with a Pan-Tilt-Zoom (PTZ) camera as its only sensing modality. The approach combines the morphological closing operation based on Sobel Edge Detection Operation and the (μ-kσ) thresholding technique to detect obstacles to soften the various lighting and ground floor effects. Both the morphology method and thresholding technique are computationally simple. The processing speed of the algorithm is fast enough to avoid some active obstacles. In addition, this approach takes into account the history obstacle effects on the current state. Fuzzy logic is used to control the behaviors of AMR as it navigates in the environment. All behaviors run concurrently and generate motor response solely based on vision perception. A priority based on subsumption coordinator selects the most appropriate response to direct the AMR away from obstacles. Validation of the proposed approach is done on a Pioneer 1 mobile robot.

Navigation of Non-holonomic Mobile Robot Using Neuro-fuzzy Logic with Integrated Safe Boundary Algorithm

In the present work, autonomous mobile robot（AMR） system is intended with basic behaviour, one is obstacle avoidance and the other is target seeking in various environments. The AMR is navigated using fuzzy logic, neural network and adaptive neurofuzzy inference system（ANFIS） controller with safe boundary algorithm. In this method of target seeking behaviour, the obstacle avoidance at every instant improves the performance of robot in navigation approach. The inputs to the controller are the signals from various sensors fixed at front face, left and right face of the AMR. The output signal from controller regulates the angular velocity of both front power wheels of the AMR. The shortest path is identified using fuzzy, neural network and ANFIS techniques with integrated safe boundary algorithm and the predicted results are validated with experimentation. The experimental result has proven that ANFIS with safe boundary algorithm yields better performance in navigation, in particular with curved/irregular obstacles.