AN IMPROVED BARE-BONES PARTICLE SWARM ALGORITHM FOR MULTI-OBJECTIVE OPTIMIZATION WITH APPLICATION TO THE ENGINEERING STRUCTURES

In this paper, an improved bare-bones multi-objective particle swarm algorithm is proposed to solve the multi-objective size optimization problems with non-linearity and constraints in structural design and optimization. Firstly, the development of particle individual guide and the randomness of gravity factor are increased by modifying the updated form of particle position. Then, the combination of spatial grid density and congestion distance ranking is used to maintain the external archive, which is divided into two parts: feasible solution set and infeasible solution set. Next, the global best positions are determined by increasing the probability allocation strategy which varies with time. The algorithmic complexity is given and the performance of solution ability, convergence and constraint processing are analyzed through standard test functions and compared with other algorithms. Next, as a case study, a support frame of triangle track wheel is optimized by the BB-MOPSO and improved BB-MOPSO. The results show that the improved algorithm improves the cross-region exploration, optimal solution distribution and convergence of the bare-bones particle swarm optimization algorithm, which can effectively solve the multi-objective size optimization problem with non-linearity and constraints

TLBO-Based Adaptive Neurofuzzy Controller for Mobile Robot Navigation in a Strange Environment

This work investigates the possibility of using a novel evolutionary based technique as a solution for the navigation problem of a mobile robot in a strange environment which is based on Teaching-Learning-Based Optimization. TLBO is employed to train the parameters of ANFIS structure for optimal trajectory and minimum travelling time to reach the goal. The obtained results using the suggested algorithm are validated by comparison with different results from other intelligent algorithms such as particle swarm optimization (PSO), invasive weed optimization (IWO), and biogeography-based optimization (BBO). At the end, the quality of the obtained results extracted from simulations affirms TLBO-based ANFIS as an efficient alternative method for solving the navigation problem of the mobile robot

A review: On path planning optimization criteria and mobile robot navigation

Mobile robots are growing more significant from time to time and have been applied to many fields such as agriculture, space, and even human life. It could improve mobile robot navigation efficiency, ensure path planning safety and smoothness, minimize time execution, etc. The main focus of mobile robots is to have the most optimal functions. An intelligent mobile robot is required to travel autonomously in various environments, static and dynamic. This paper article presents the optimization criteria for mobile robot path planning to figure out the most optimal mobile robot criteria to fulfill, including modeling analysis, path planning and implementation. Path length and path smoothness are the most parameters used in optimization in mobile robot path planning. Based on path planning, the mobile robot navigation is divided into three categories: global navigation, local navigation and personal navigation. Then, we review each category and finally summarize the categories in a map and discuss the future research strategies

Mobile Robot Path Planning Based on Ant Colony Algorithm With A* Heuristic Method

This paper proposes an improved ant colony algorithm to achieve efficient searching capabilities of path planning in complicated maps for mobile robot. The improved ant colony algorithm uses the characteristics of A* algorithm and MAX-MIN Ant system. Firstly, the grid environment model is constructed. The evaluation function of A* algorithm and the bending suppression operator are introduced to improve the heuristic information of the Ant colony algorithm, which can accelerate the convergence speed and increase the smoothness of the global path. Secondly, the retraction mechanism is introduced to solve the deadlock problem. Then the MAX-MIN ant system is transformed into local diffusion pheromone and only the best solution from iteration trials can be added to pheromone update. And, strengths of the pheromone trails are effectively limited for avoiding premature convergence of search. This gives an effective improvement and high performance to ACO in complex tunnel, trough and baffle maps and gives a better result as compare to traditional versions of ACO. The simulation results show that the improved ant colony algorithm is more effective and faster

Adaptive path planning for fusing rapidly exploring random trees and deep reinforcement learning in an agriculture dynamic environment UAVs

Unmanned aerial vehicles (UAV) are a suitable solution for monitoring growing cultures due to the possibility of covering a large area and the necessity of periodic monitoring. In inspection and monitoring tasks, the UAV must find an optimal or near-optimal collision-free route given initial and target positions. In this sense, path-planning strategies are crucial, especially online path planning that can represent the robot’s operational environment or for control purposes. Therefore, this paper proposes an online adaptive path-planning solution based on the fusion of rapidly exploring random trees (RRT) and deep reinforcement learning (DRL) algorithms applied to the generation and control of the UAV autonomous trajectory during an olive-growing fly traps inspection task. The main objective of this proposal is to provide a reliable route for the UAV to reach the inspection points in the tree space to capture an image of the trap autonomously, avoiding possible obstacles present in the environment. The proposed framework was tested in a simulated environment using Gazebo and ROS. The results showed that the proposed solution accomplished the trial for environments up to 300 m3 and with 10 dynamic objects.The authors would like to thank the following Brazilian Agencies CEFET-RJ, CAPES, CNPq, and FAPERJ. The authors also want to thank the Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança–IPB (UIDB/05757/2020 and UIDP/05757/2020), the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI, and Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC) and IPB, Portugal. This work was carried out under the Project “OleaChain: Competências para a sustentabilidade e inovação da cadeia de valor do olival tradicional no Norte Interior de Portugal” (NORTE-06-3559-FSE-000188), an operation to hire highly qualified human resources, funded by NORTE 2020 through the European Social Fund (ESF).info:eu-repo/semantics/publishedVersio

Heterogeneous Multi-Robot Collaboration for Coverage Path Planning in Partially Known Dynamic Environments

This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAVs) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios. This paper defines specific roles for the UAVs and UGV within the framework to address challenges like partially known terrains and dynamic obstacles. The UAVs are focused on aerial inspections and mapping, while UGV conducts ground-level inspections. In addition, the UAVs can return and land at the UGV base, in case of a low battery level, to perform hot swapping so as not to interrupt the inspection process. This research mainly emphasizes developing a robust Coverage Path Planning (CPP) algorithm that dynamically adapts paths to avoid collisions and ensure efficient coverage. The Wavefront algorithm was selected for the two-dimensional offline CPP. All robots must follow a predefined path generated by the offline CPP. The study also integrates advanced technologies like Neural Networks (NN) and Deep Reinforcement Learning (DRL) for adaptive path planning for both robots to enable real-time responses to dynamic obstacles. Extensive simulations using a Robot Operating System (ROS) and Gazebo platforms were conducted to validate the approach considering specific real-world situations, that is, an electrical substation, in order to demonstrate its functionality in addressing challenges in dynamic environments and advancing the field of autonomous robots.The authors also would like to thank their home Institute, CEFET/RJ, the federal Brazilian research agencies CAPES (code 001) and CNPq, and the Rio de Janeiro research agency, FAPERJ, for supporting this work.info:eu-repo/semantics/publishedVersio

Dağıtık mobil robotlar için yeni bir otonom yol planlama ve engel tespit sisteminin tasarımı

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Endüstride mobil robotlar genellikle çalışma sahası içerisindeki iş istasyonları arasında yük taşıma amacıyla kullanılmaktadır. Bu yüklerin geleneksel metodlar kullanılarak forkliftler ile taşınması durumunda operatöre bağlı hata ve kazalar oluşabilmektedir. Ayrıca forkliftlerin hareket kısıtlılıkları da yüklerin transferleri sırasında sınırlı çalışma alanı oluşturmaktadır. Otonom mobil robotların endüstriyel sahalarda yer alması bu hata ve kazaların azalması konusunda önem arz etmektedir. Mobil robotların yaygın kullanım alanlarından birisi de stoklama hizmetinin verildiği depolardır. Genellikle robot filoları şeklinde kullanılan bu robotlar için ticari gelişmelerle beraber birçok akademik çalışmalarda gerçekleştirilmektedir. Bu çalışmalardan lokalizasyon ve pozisyonlama problemlerinin çözülmesi önemli bir yer almaktadır. Robotların belirlenen görevleri yerine getirirken en az güç tüketimi ile rota boyunca güvenli bir biçimde ilerlemesi, akademik ve ticari çalışmaların temelini oluşturmaktadır. Bu tez çalışmasında, dağıtık hareket kabiliyetine sahip mobil robotların yol planlaması üzerine olasılıksal parçacık filtre tabanlı algoritmalara dayanan bir çalışma yürütülmüştür. Çalışmada Eş Zamanlı Konum Belirleme ve Haritalama (SLAM) ile Doluluk Kafesi metotları kullanılarak mobil robotların bulunduğu sahanın 2 boyutlu haritası oluşturulmuştur. Bu methotlar kullanılarak çıkarılan harita içerisindeki mobil robotların belirlenen bir başlangıç noktasından hedef noktaya Adaptif Monte Carlo Lokalizasyon (AMCL) algoritması kullanarak ulaşması sağlanmıştır. Robotlar tarafından bilinen harita üzerinde hedef noktaya ulaşma aşamaları ve navigasyon parametreleri gerçek zamanlı olarak bir sunucu istasyon üzerinden kontrol edilmektedir. Bu işlemler oluşturulan bir kablosuz ağ üzerinden SSH (Secure Shell) protokolü ile gerçekleştirilmektedir. Harita üzerinde oluşturulan dinamik rota, robotlar tarafından takip edildiği esnada oluşabilecek hareketli engellerden robotların kaçınımı kinect kameradan alınan anlık görüntüler ile sağlanmaktadır. Gerçekleştirilen bu lokal yol planlaması için ise Dinamik Pencere Algoritmasından yararlanılmıştır. Tez çalışmasında, modern algoritma ve donanımları kullanarak haritalama, pozisyonlama ve konumlama gibi birçok fonksiyonu yerine getirebilen bir sistem tasarımı ve prototipi gerçekleştirilmiştir. Bu yönüyle çalışma, Endüstri 4.0 uygulamalarının arttığı günümüzde birçok sektörde ihtiyaç hissedilen farklı yapı ve özellikteki mobil robotların ARGE çalışmaları için faydalı olabilecek niteliktedir. Anahtar kelimeler: Otonom robot, Adaptif Monte Carlo Lokalizasyonu, yol planlama, engel algılama, robot işletim sistemi.In industry, mobile robots are generally used for load transportation between workstations in the factory. If these loads are carried by forklifts using conventional methods, errors and accidents may occur due to the operator. Furthermore, the movement limitations of the forklifts also create a limited working area during the transfer of loads. The use of autonomous mobile robots in industrial areas is important in reducing these errors and accidents. One of the common uses of mobile robots that are sensitive to environmental factors and can work autonomously is the warehouses where the stocking service is provided. For these robots, which are usually used as robot fleets is carried out in many academic studies along with commercial developments. Solving localization and positioning problems is an important part of these studies. The fact that robots move safely along the route with minimal power consumption while performing the specified tasks constitutes the basis of academic and commercial studies. In this thesis, a study based on probabilistic particle filter algorithms has been carried out on the path planning of mobile robots with distributed mobility. In the study, firstly, 2D map of the field where robots are located have been mapping by SLAM and Occupancy Grid Mapping methods. Using these methods, the multiple mobile robots within the map were accessed from a designated starting point to the destination using the Adaptive Monte Carlo Localization algorithm. The steps to reach the destination on the map known by the robots and the navigation parameter are controlled in real time via a master station. These processes are performed via SSH protocol over a wireless network. The dynamic obstacle avoidance of the robots is provided by realtime images taken from the kinect camera while route on the map. For this local path planning, Dynamic Window Algorithm was used. In the thesis, a system design and prototype which can perform many functions such as mapping, positioning and localization using modern algorithms and equipments have been achieved. In this respect, the thesis will contribute for R & D studies of mobile robots of different structures and features which are needed in many sectors those Industry 4.0 applications. Keywords: Autonomous robot, Adaptive Monte Carlo Localization, path planning, obstacle avoidance, robot operating syste

Dynamic robot path planning using an enhanced simulated annealing approach

Evolutionary computation is an effective tool for solving optimization problems. However, its significant computational demand has limited its real-time and on-line applications, especially in embedded systems with limited computing resources, e.g., mobile robots. Heuristic methods such as the genetic algorithm (GA) based approaches have been investigated for robot path planning in dynamic environments. However, research on the simulated annealing (SA) algorithm, another popular evolutionary computation algorithm, for dynamic path planning is still limited mainly due to its high computational demand. An enhanced SA approach, which integrates two additional mathematical operators and initial path selection heuristics into the standard SA, is developed in this work for robot path planning in dynamic environments with both static and dynamic obstacles. It improves the computing performance of the standard SA significantly while giving an optimal or near-optimal robot path solution, making its real-time and on-line applications possible. Using the classic and deterministic Dijkstra algorithm as a benchmark, comprehensive case studies are carried out to demonstrate the performance of the enhanced SA and other SA algorithms in various dynamic path planning scenarios

Dynamic robot path planning using an enhanced simulated annealing approach

Evolutionary computation is an effective tool for solving optimization problems. However, its significant computational demand has limited its real-time and on-line applications, especially in embedded systems with limited computing resources, e.g., mobile robots. Heuristic methods such as the genetic algorithm (GA) based approaches have been investigated for robot path planning in dynamic environments. However, research on the simulated annealing (SA) algorithm, another popular evolutionary computation algorithm, for dynamic path planning is still limited mainly due to its high computational demand. An enhanced SA approach, which integrates two additional mathematical operators and initial path selection heuristics into the standard SA, is developed in this work for robot path planning in dynamic environments with both static and dynamic obstacles. It improves the computing performance of the standard SA significantly while giving an optimal or near-optimal robot path solution, making its real-time and on-line applications possible. Using the classic and deterministic Dijkstra algorithm as a benchmark, comprehensive case studies are carried out to demonstrate the performance of the enhanced SA and other SA algorithms in various dynamic path planning scenarios

Control of free-ranging automated guided vehicles in container terminals

Container terminal automation has come to the fore during the last 20 years to improve their efficiency. Whereas a high level of automation has already been achieved in vertical handling operations (stacking cranes), horizontal container transport still has disincentives to the adoption of automated guided vehicles (AGVs) due to a high degree of operational complexity of vehicles. This feature has led to the employment of simple AGV control techniques while hindering the vehicles to utilise their maximum operational capability. In AGV dispatching, vehicles cannot amend ongoing delivery assignments although they have yet to receive the corresponding containers. Therefore, better AGV allocation plans would be discarded that can only be achieved by task reassignment. Also, because of the adoption of predetermined guide paths, AGVs are forced to deploy a highly limited range of their movement abilities while increasing required travel distances for handling container delivery jobs. To handle the two main issues, an AGV dispatching model and a fleet trajectory planning algorithm are proposed. The dispatcher achieves job assignment flexibility by allowing AGVs towards to container origins to abandon their current duty and receive new tasks. The trajectory planner advances Dubins curves to suggest diverse optional paths per origin-destination pair. It also amends vehicular acceleration rates for resolving conflicts between AGVs. In both of the models, the framework of simulated annealing was applied to resolve inherent time complexity. To test and evaluate the sophisticated AGV control models for vehicle dispatching and fleet trajectory planning, a bespoke simulation model is also proposed. A series of simulation tests were performed based on a real container terminal with several performance indicators, and it is identified that the presented dispatcher outperforms conventional vehicle dispatching heuristics in AGV arrival delay time and setup travel time, and the fleet trajectory planner can suggest shorter paths than the corresponding Manhattan distances, especially with fewer AGVs.Open Acces