Robot Travel Research Articles

KOALA: A Modular Dual-Arm Robot for Automated Precision Pruning Equipped with Cross-Functionality Sensor Fusion

Landscape maintenance is essential for ensuring agricultural productivity, promoting sustainable land use, and preserving soil and ecosystem health. Pruning is a labor-intensive task among landscaping applications that often involves repetitive pruning operations. To address these limitations, this paper presents the development of a dual-arm holonomic robot (called the KOALA robot) for precision plant pruning. The robot utilizes a cross-functionality sensor fusion approach, combining light detection and ranging (LiDAR) sensor and depth camera data for plant recognition and isolating the data points that require pruning. The You Only Look Once v8 (YOLOv8) object detection model powers the plant detection algorithm, achieving a 98.5% pruning plant detection rate and a 95% pruning accuracy using camera, depth sensor, and LiDAR data. The fused data allows the robot to identify the target boxwood plants, assess the density of the pruning area, and optimize the pruning path. The robot operates at a pruning speed of 10–50 cm/s and has a maximum robot travel speed of 0.5 m/s, with the ability to perform up to 4 h of pruning. The robot’s base can lift 400 kg, ensuring stability and versatility for multiple applications. The findings demonstrate the robot’s potential to significantly enhance efficiency, reduce labor requirements, and improve landscape maintenance precision compared to those of traditional manual methods. This paves the way for further advancements in automating repetitive tasks within landscaping applications.

Simultaneous allocation and sequencing of orders for robotic mobile fulfillment system using reinforcement learning algorithm

Robotic Mobile Fulfillment Systems (RMFS) can benefit large e-commerce warehouse operations significantly. To fulfill the orders received, RMFS deploys mobile robots to carry shelves back and forth from the storage area to the picking station. Order allocation and sequencing for mobile robots is a complex yet critical task as it influences the distance traveled by mobile robots in fulfilling the orders, i.e., appropriately allocating and sequencing the orders. In this paper, a Simultaneous Allocation and Sequencing of Orders Reinforcement Learning (SASORL) algorithm is proposed to minimize the distance traveled by mobile robots. Unlike existing methods, the SASORL algorithm optimizes order allocation and sequencing concurrently, significantly reducing mobile robot travel distance. The proposed SASORL algorithm encompasses three sets, namely state, action, and reward/penalty. The state set comprises the orders fulfilled, whereas the action set contains the orders yet to be fulfilled. The distance traveled by the mobile robot as a result of the orders allocated and sequenced is taken as the penalty for the proposed SASORL algorithm. As the proposed SASORL algorithm simultaneously allocates and sequences the orders to the mobile robots, the action set depletes, the state set enlarges, and the penalty updates until the action set becomes null. Each episode restarts with the learned experience of the prior episodes, and after completing a few episodes, the SASORL algorithm is capable of generating an optimal order allocation and sequence that commits the minimum travel distance to the mobile robots. SASORL algorithm is superior to widely adopted soft computing techniques when orders are randomly distributed. This superiority is evidenced by a 26% reduction in the maximum distance traveled by all mobile robots, a 54% reduction in the standard deviation of the distance traveled by the mobile robots, and a marginal increase of 7% in the total distance traveled by all mobile robots. Moreover, the SASORL algorithm outperforms soft computing techniques by 44% in terms of computation time.

Development of Pesticide Sprayer Robot Prototype for Chilli Farm Agricultural Application

Accidental spills of chemicals, leakages, poor spraying equipment,or a lack of knowledge about personal protection equipment usage can expose farm workers to pesticides either in open fields or greenhouse.Pesticides may affect their health, particularly on dermal contact and respiratory system. Besides that, traditional pest management for massive agriculture industry requires a big number of employees, large quantities of pesticides, and the risk of inefficient and uneven pesticides coating. Therefore, a pesticide spraying robot, which is equipped with two ultrasonic sensors is developed to spray pesticide independently and allows the workers to maximize pesticide usage on the agricultural application such as chilli farm. The prototype is designed to automatically moves and operate in two modes, either automatic or manual. For automatic mode, the robot travel and operated independently without human control or intervention. While for manual mode, the Blynk application is used to remotely control the robot’s movement and spraying insecticide. In this research, the garlic extract and dishwashing liquid detergent were used as organic insecticides since they are effective and safe for plants and the environment. The study discovered that for a 50 cm two-month chilli plant, a three-seconds pesticide setting was appropriate, with 30 ml pesticide applied per chilli plant.

Trial of Autonomous Mobile Robot Travel Control via Local 5G

Trial of Autonomous Mobile Robot Travel Control via Local 5G

Autonomous mobile robot travel under deadlock and collision prevention algorithms by agent-based modelling in warehouses

ABSTRACT Recent dramatic increase in e-commerce has also increased the adoption of automation technologies in warehouses. Autonomous mobile robots (AMRs) are from those technologies widely utilized in warehouse operations. It is important to design the operation of those robotic systems in such a way that, they meet the current and future system requirements correctly. In this paper, we study flexible travel of AMRs in warehouses by developing smart deadlock and collision prevention algorithms on agent-based modelling. By that, AMR agents can interact with each other and environment, so that they can make smart decisions maximizing their goals. We compare the performance of the developed flexible travel system with non-flexible designs where there is a single AMR dedicated to a specific zone so that no deadlock or collision possibility takes place. The results show that AMRs may provide up to 39% improvement in the flexible system compared to its non-flexible design.

Multiple Mobile Robots Coordination in Shared Workspace for Task Makespan Minimization

In this paper we consider a system of multiple mobile robots (MMRS) and the process of their concurrent motion in a shared two-dimensional workspace. The goal is to plan the robot movement along given fixed paths so as to minimize the completion time of all the robots while ensuring that they never collide. Thus, the considered problem combines the problems of robot schedule optimization with collision and deadlock avoidance. The problem formulation is presented and its equivalent reformulation that does not depend explicitly on the geometry of the robot paths is proposed. An event-based solution representation is proposed, allowing for a discrete optimization approach. Two types of possible deadlocks are identified and deadlock avoidance procedures are discussed. We proposed two types of solving methods. First, we implemented two metaheuristics: the local-search-based taboo search as well as the population-based artificial bee colony. Next, we implemented 14 simple constructive algorithms, employing dispatch rules such as first-in first-out, shortest distance remaining first, and longest distance remaining first, among others. A set of problem instances for different numbers of robots is created and provided as a benchmark. The effectiveness of the solving methods is then evaluated by simulation using the generated instances. Both deterministic and lognormal-distributed uncertain robot travel times are considered. The results prove that the taboo search metaheuristic obtained the best results for both deterministic and uncertain cases, with only artificial bee colony and a few constructive algorithms managing to remain competitive. Detailed results as well as ideas to further improve proposed methods are discussed.

Robot Routing Problem of Last-Mile Delivery in Indoor Environments

With the development of robot technology, trials adopting robots for last-mile delivery are continuing, and the final destination of last-mile delivery is further expanding into indoor environments. Unlike existing studies conducted for robot-based last-mile delivery in outdoor environments, two main issues must be solved to enable last-mile delivery in indoor environments using robots. First, it is necessary to reasonably and realistically estimate the robot travel time considering horizontal and vertical movement segments within a given building. Second, optimizing the robot routing problem based on the estimated robot travel time is necessary. In this paper, we proposed a new method to estimate the robot travel time considering robot movement characteristics and an elevator in a building. In addition, we developed a mathematical model of the robot routing problem and problem-specific heuristic based on a genetic algorithm to quickly solve the proposed mathematical model. It obtained the exact solutions when the problem size was small and near-optimal solutions in the medium- and large-sized problems (average optimality gap: 0.11% and 0.18%, respectively). Through extensive experiments assuming various building structures, it was determined that the proposed model and heuristic can quickly yield realistic solutions for indoor robot-based last-mile delivery.

Mobile robot path planning using fuzzy enhanced improved Multi-Objective particle swarm optimization (FIMOPSO)

This paper introduces a method for car-like mobile robot path planning (CRPP). The robot works in both dynamic and static situations. The aim of this method is to explore the best safe path with minimum path length, minimum motor torque, minimum travel time, minimum robot acceleration and maximum obstacle avoidance. Kinodynamic and non-holonomic constraints related with car-like robot are considered. Fuzzy enhanced Improved Multi-objective Particle Swarm Optimization (FIMOPSO) algorithm is proposed to solve the CRPP problem. Fuzzy inference system is used for obstacle avoidance. In the proposed FIMOPSO, five improvements are made. Proposed technique is compared with Multi-objective Strength Pareto Evolutionary Algorithm 2 (MOSPEA2) technique. Experiments on a custom-made car-like robot are ensuring the quality of proposed technique. This research works show that proposed FIMOPSO is another alternative technique to CRPP problems. Paths dictated by FIMOPSO are safe, collision free, feasible, and possible and can be practically implemented. Fuzzy inference system works well for safe robot travel. FIMOPSO simulation paths are acceptable. Since, the deviation between experiment and simulation is less than 2%.

Dynamic Waypoint Navigation: Model-Based Adaptive Trajectory Planner for Human-Symbiotic Mobile Robots

Path planning in dynamic environments is still a challenging issue with autonomous mobile robots. Current methods lack adaptability to various passing scenarios, a variety of passing trajectories including an acceleration path, or immediacy in planning time, which require human-aware navigation. In this study, we propose Dynamic Waypoint Navigation (DWN), which is a model-based adaptive real-time trajectory planning method. DWN first predicts human-robot path interference and the time and position of the interference on the basis of the measured velocity of humans. It then dynamically designates several waypoints considering the time delay of both calculation time and robot travel time. Then, DWN generates several trajectories by combining different speeds (default, acceleration, and deceleration) and paths (default, right, and left) and selects the best trajectory in terms of an interference-avoidance energy cost based on the degree of velocity-vector change. DWN can also output a trajectory within 0.5 s to immediately adapt to changes in human behavior and adopt a simple mathematical model and algorithm to enable easy expansion. Simulation and experimental results reveal that the DWN can adequately select a time-efficient trajectory in real-time and adaptively change a trajectory depending on human movement.

Street art as a cultural and educational project

The necessity of discovering ways to form and develop value orientations of the younger generation makes the research topical. Value orientations are formed under the influence of various sociocultural factors, but there is also a regulated factor – upbringing. In upbringing and education, we can use communication through art. The purpose of this study was to determine the possibilities of a street art project as a means of forming and developing value orientations of the younger generation. The study was conducted in 2015-2021 within the framework of the projects: “Odessa Street Art Festival”, “Travel of the TIS robot in time”, “Along the paths of the Jewish history”, “Flying over Odessa”. The idea of creating a street art object as a cultural and educational project belongs to Aleksey Shkurat, the head of the Peach art studio. The survey method (questioning and conversation) was used to analyze the value orientations of children and adolescents who took part in the experiment (387 participants). The experimental work was divided into three stages. At the first stage, we conducted a pre-experimental research, which was to identify the initial value orientations of children and adolescents from 7 to 15 years old. The respondents filled in the questionnaire with the measured value categories “education as a value”, “art as a value”, “culture as a value”. The second stage of the experiment was direct involvement in the projects. The third stage of our experimental work was the formative experiment. We made a questionnaire and sent it by e-mail to all the respondents. The results of processing the data obtained indicate the positive dynamics of the respondents’ value orientations development. Compared with the results of the pre-experimental research, the level of the value orientations development (measured value categories “education as value”, “art as value”, “culture as value”) increased by 38%. The results of the experiment proved that the use of a street art project has a positive effect on the children and adolescents’ value orientations development.

Wheeled Robot Development for Obstruction Avoidance Mechanism

Abstract: Consolidating the current improvement status of clever robots, impediment aversion and programmed following are the focal point of robot travel issues. In light of the improvement status of homegrown clever control frameworks, current innovation, and so on, this paper utilizes Arduino as the centre control framework, joined with an infrared following module. Four modules, for example, ultrasonic impediment aversion module, engine drive module and force module have planned a decent control conspire, accordingly understanding the insightful following and hindrance evasion capacity of the wheeled robot. Keywords: Arduino, Wheel robot, Tracking, Obstacle Avoidance

Trajectory Generation of an Industrial Robot With Constrained Kinematic and Dynamic Variations for Improving Positional Accuracy

The joint trajectory of the robot needs to be computed in an optimal manner for proper torch orientation, smooth travel of the robot along the trajectory path. This can be achieved by limiting the travel time, kinematic and dynamic variations of the robot joints like the jerks, and torque induced in the joints in the travel of the robot. As the objectives of total travel time and joint jerk and torque rate are contradictory functions, non-dominated sorting genetic algorithm-II (NSGA-II) approach has been used to obtain the pareto front consisting of optimal solutions. The fuzzy membership function has been used to obtain the optimal solution from the pareto front with best trade-off between objectives for further optimal trajectory generation. From the simulation results, it can be concluded that the proposed approach can be effectively used for optimal trajectory planning of Kawasaki RS06L industrial manipulator with minimal jerk, torque rate, and total travel time for smooth travel of robot with higher positional accuracy.

Approaches to motion control intellectualization of a mobile robot

The paper considers the principles of intelligent motion control of mobile robots using the example of omni-wheel modules. The proposed design solution uses components of movement intelligence in any direction, receiving commands from a human operator or above a standing automatic control device, consisting of an angle of movement direction and the required distance of movement. This paper presents an embodiment of using omni-wheels to move a mobile robot over a flat surface. Features of device and application of drive with three omni-wheels in comparison with differential drive are considered. Kinematics, basic principles of motion control formation, hardware and software complex for its implementation are described. There were revealed two alternative methods of organization of drive control in conditions of shortage of low-level hardware resources on the basis of 8-bit microcontroller, their advantages and disadvantages have been analyzed. Process support and materials have been presented that allows realizing the competitive advantages of development while minimizing the cost of components. Features of mobile robot travel route development have been mentioned on the example of competitive practice.

Deterministic predictive dynamic scheduling for crop-transport co-robots acting as harvesting aids

Manual harvesting of fresh-market crops like strawberries is very labor-intensive. Apart from picking fruits, pickers spend significant amounts of time carrying full trays to a collection station at the edge of the field. Small teams of harvest-aid robots that help large picking crews by transporting empty and full trays can increase harvest efficiency by reducing pickers’ non-productive walking times. However, robot sharing among the crew may introduce non-productive waiting delays between the time a tray becomes full and when a robot arrives to collect it. Reactive robot scheduling cannot eliminate mean waiting times because pickers must wait for a robot to travel the distance from the collection station to them. Predictive scheduling is better suited to this task, because if the time and location that a pickers’ tray will fill are known to the scheduler in advance, a robot can start moving toward that location before the tray becomes full; hence, waiting times due to robot travel can be reduced or eliminated.In this paper, dynamic predictive scheduling was modeled for teams of robots carrying trays during manual harvesting. The times and locations of the tray-transport requests were assumed to be known exactly (deterministic predictions). Near-optimal scheduling was implemented to provide efficiency upper-bounds for any predictive scheduling algorithms that incorporate uncertainty in the predictive requests. Robot-aided harvesting was simulated using manual-harvest data collected from a commercial picking crew. Scheduling performance was studied as a function of the number of robots – for a given crew size – with robot speed as a parameter. Additionally, the effect of the earliness of the availability of the predictions on performance was studied.Experimental results showed that both reactive and predictive scheduling did not improve the mean non-productive time significantly relative to manual harvesting, when only four robots were used. Actually, deploying fewer than four robots led to worse non-productive time. However, introducing five to eight robots decreased mean non-productive time drastically, and when ten or more robots were used, non-productive time was reduced by 64.6% (reactive scheduling) and up to 93.7% (predictive scheduling) with respect to all-manual non-productive time. The efficiency increases were 15% and 24%, respectively. It was also verified that reactive dispatching always performed worse than deterministic predictive scheduling. Also, when the robot-to-picker ratio was larger than approximately 1:3, the waiting time and efficiency plateaued, i.e., did not improve further, regardless of how early the prediction was available to the scheduler. The reason is that the mean waiting time is lower bounded by the sum of mean travel time and tray exchange time, which are both constant. Although the above results represent upper-bounds for performance – since predictions were perfect - they indicate that tray-transport robots acting as harvest aids can increase harvesting efficiency significantly when scheduled properly.

Autonomous robot for cleaning photovoltaic panels in desert zones

In desert zones, a continuous cleaning activity of photovoltaic panels in solar plants is required since the deposition of both airborne dust and sand after a storm can reduce their efficiency up to 80%. Manual cleaning of the photovoltaic panels in dry areas is costly, cannot make use of water and workers must be employed several times in a month, often under extreme environmental conditions. For all these reasons, the research of cleaning solutions performed by autonomous robotic systems are seen beneficial to recover the solar panels efficiency at reasonable costs also nightly. In this respect, this paper presents the implementation of an unmanned low-cost robotic device operating without rails or guides for waterless dust and sand removal from the surface of photovoltaic panels. The robot autonomously moves as a half-track and gently wipes the dust away by means of a couple of independent helical brushes that alternatively rotate, according to a defined combination of the cleaning and motion strategy. Moreover, in real-time the robot detects its spatial position by ultrasonic sensors, regulates both its speed rate and motion direction. The on-board control system, based on an ARDUINO DUE platform, settles the robot travel direction, its speed and which brush must be activated for cleaning. Experimental tests have been performed by fabricating a prototype of the device to assess its effectiveness and reliability in terms of the autonomous motion, dust removal and low power consumption.

Design and simulation analysis on underwater robots like reptiles

In order to clean up debris with a certain depth of water and a stepped section, an underwater mobile robot with four legs walking was designed. Used the engineering method to calculate the force of the robot roller cutter, and the step of one motion cycle is calculated by the geometric relationship of the robot travel mechanism. The hydrodynamics simulation was carried out using simulation analysis. The results show that the maximum horizontal displacement of a robot during a motion cycle is 0.45m; the buoyancy water tank is more buoyant than gravity when it is waterless; the local maximum stress and displacement of the drum cutter and the main frame meet the working requirements; the corresponding hydraulic cylinder provides more driving force than the hydraulic cylinder resistance. This four-foot dredging robot can provide reference for the underwater motion of the multi-foot underwater robot and its water resistance analysis.

A simulated multi-objective model for flexible job shop transportation scheduling

This paper proposes a new dynamic algorithm based on simulation approach and multi-objective optimization to solve the FJSP with transportation assignment. The objectives considered in scheduling jobs and transportation tasks in a flexible job shop manufacturing system include makespan, robot travel distance, time difference with due date and critical waiting time. The results obtained from the computational experiments have shown that the proposed approach is efficient and competitive.

DDM: Fast Near-Optimal Multi-Robot Path Planning Using Diversified-Path and Optimal Sub-Problem Solution Database Heuristics

We propose a novel centralized and decoupled algorithm, DDM, for solving multi-robot path planning problems in grid graphs, targeting on-demand and automated warehouse-like settings. Two settings are studied: a traditional one whose objective is to move a set of robots from their respective initial vertices to the goal vertices as quickly as possible, and a dynamic one which requires frequent re-planning to accommodate for goal configuration adjustments. Among other techniques, DDM is mainly enabled through exploiting two innovative heuristics: path diversification and optimal sub-problem solution databases. The two heuristics attack two distinct phases of a decoupling-based planner: while path diversification allows the more effective use of the entire workspace for robot travel, optimal sub-problem solution databases facilitate the fast resolution of local path conflicts. Extensive evaluation demonstrates that DDM achieves high levels of scalability and high levels of solution optimality.

Intelligent Tracking Obstacle Avoidance Wheel Robot Based on Arduino

Abstract Combining the current development status of intelligent robots, obstacle avoidance and automatic tracking are the focus of robot travel problems.Based on the development status of domestic intelligent control systems, current technology, etc, this paper uses arduino as the core control system, combined with infrared tracking module. Four modules, such as ultrasonic obstacle avoidance module, motor drive module and power module, have designed a good control scheme, thus realizing the intelligent tracking and obstacle avoidance function of the wheeled robot.

Optimal trajectory generation of an industrial welding robot with kinematic and dynamic constraints

PurposeThis paper aims to present an optimal trajectory planning for industrial MOTOMAN MA1440A gas metal arc welding system. A new and efficient evolutionary algorithm, enhanced multi-objective teaching learning-based optimization (EMOTLBO) method, i.e. TLBO with non-dominated sorting approach has been proposed to obtain the optimal joint trajectory for the defined weld seam path.Design/methodology/approachThe joint trajectory of the welding robot need to be computed in an optimal manner for proper torch orientation, smooth travel of the robot along the weld path and for achieving higher positional accuracy. This can be achieved by limiting the kinematic and dynamic variations of the robot joints like joint jerks, squared acceleration and torque induced in the joints while travel of the robot along the weld path. Also, the robot travel should be done within minimum possible time for maintaining productivity. This leads to a multi-objective optimization problem which needs to be solved for maintaining proper orientation of the robot end effector. EMOTLBO has been proposed to obtain the Pareto front consisting of optimal solutions. The fuzzy membership function has been used to obtain the optimal solution from the Pareto front with best trade-off between objectives.FindingsThe proposed method has been implanted in MATLAB R2017a for simulation results. The joint positions have been used to program the robot for performing welding operation along the weld seam. From the simulation and experimental results, it can be concluded that the proposed approach can be effectively used for optimal trajectory planning of MOTOMAN MA 1440 A arc welding robot system as a very smooth and uniform weld bead has been obtained with maximum weld quality.Originality/valueIn this paper, a novel approach for optimal trajectory planning welding arc robot has been performed. Though trajectory planning of industrial robots has been done before, it has not been done yet for welding robot. The objectives are formulated taking in consideration of requirement of welding process like minimization of joint jerks and torques induced during welding operation due to travel of robot with the effect of arc spatter, minimization of squared acceleration for maintaining constant joint velocity and finally minimization of total travel time for maintaining productivity.