Search

Aiming at the problem of the wide range and great difficulty in the future of battlefield airspace control, based on the unique advantages of an airspace grid system in an airspace grid representation and time–space binary computing, this paper designs a pre-clustering method for mission airspace based on airspace location correlation under the condition of future large-scale air combat missions in order to realize the block control of battlefield airspace. This method reduces the whole 3D battlefield space projection to a 2D plane and regards the task airspace projection as “obstacles” in the task area; Multi-A * algorithm is used to generate the airspace clustering line surrounding the task airspace, and the airspace association clustering problem is transformed into a multiple “start point-end point” path planning problem with autonomous optimization. Through the experiment, it was found that clustering the airspace can effectively improve the management and control efficiency of large-scale battlefield airspace.

In modern transportation, traffic congestion has become an urgent problem in large and medium-sized cities. In smart transportation systems, it is an effective solution to design load balancing path planning algorithms that can dynamically adapt to traffic conditions in order to avoid congestion. In this work, a traffic path planning algorithm based on data prediction (TPPDP) is proposed to find the path with the shortest travel time, which is built on a predictive model based on historical traffic data and current traffic information. Furthermore, a path planning algorithm based on data prediction with load balancing (TPPDP-LB) is also proposed, which combines the predicted information and the number of concurrent requests to achieve the path with shortest travel time while maintaining global load balancing. A specific distributed computing framework for TPPDP-LB algorithm is designed to reduce the runtime of the algorithm. The simulation results proved that both TPPDP and TPPDP-LB algorithms have the advantage of shortest travel time, and TPPDP-LB algorithm achieves load balancing of computing. It is also proved that the distributed computing framework designed for TPPDP-LP algorithm can effectively reduce the runtime of system as well as keep the accuracy of algorithm.

One of the keys in time-dependent routing is determining the weight for each road network link based on symmetrical and complete traffic information. To facilitate travel planning considering traffic situations based on historical global position system (GPS) trajectory data which uncover the whole road network, this paper proposes a fuzzy random forest-based road section data estimation method, which uses the third law of geography as the core idea. For different time periods, road grade, tidal lane, proximity to infrastructure (main places that affect traffic, such as schools, hospitals), and accident road sections were selected as indicators that influence the traffic. The random forest algorithm is used to build the mapping relationship between attribute data with average traffic which is obtained based on GPS data. Subsequently, the fuzzy reasoning method is used to obtain the weight of road links missing traffic information by calculating their similarities with typical road section samples. Using the road network of Suzhou City as an example, the proposed method was used to analyze estimate the average driving speeds of road sections with missing traffic information for different time periods. Experimental results show that this method can effectively avoid congested road sections and obtain high-speed travel routes.

This study investigates the task assignment problem where a fleet of dispersed vehicles needs to visit multiple target locations in a time-invariant drift field with obstacles while trying to minimise the vehicles' total travel time. The vehicles have different capabilities, and each kind of vehicles can visit a certain type of the target locations; each target location might require to be visited more than once by different kinds of vehicles. The task assignment problem has been proven to be NP-hard. A path planning algorithm is first designed to minimise the time for a vehicle to travel between two given locations through the drift field while avoiding any obstacle. The path planning algorithm provides the travel cost matrix for the target assignment, and generates routes once the target locations are assigned to the vehicles. Then, a distributed algorithm is proposed to assign the target locations to the vehicles using only local communication. The algorithm guarantees that all the visiting demands of every target will be satisfied within a total travel time that is at worst twice of the optimal when the travel cost matrix is symmetric. Numerical simulations show that the algorithm can lead to solutions close to the optimal.

V magistrskem delu je predstavljena metoda napovedovanja in izogibanja kolizij robotskega mehanizma z replaniranjem trajektorij v realnem času. Z zasnovano metodo želimo izboljšati varnost in časovno učinkovitost kolaborativnih robotskih aplikacij. Predstavljene so tehnologije, ki so potrebne za zaznavo okolja, replaniranja poti in trajektorije, napovedovanja kolizij in vodenja robota. Pri zaznavanju kolizij je uporabljeno orodje GPU-Voxels, za namene planiranja robotskih poti pa je uporabljena knjižnica OMPL. Prikazani so eksperimentalni rezultati algoritmov planiranja in izogibanje robotskega mehanizma statičnim in dinamičnim oviram. Predstavljene so primerjave sistema z metodo brez replaniranja, ki kolizije preprečuje z ustavljanjem robota, ter uporabnost sistema pri kolaborativnih aplikacijah. The master's thesis describes a method for collision prediction and collision avoidance of a robot with real-time trajectory replanning. Designed method improves safety and time efficiency of collaborative robot applications. Overview of different technologies necessary for depth and environment sensing, path and trajectory replanning, collision prediction and robot controlling is given. GPU-Voxels software is used for collision detection and OMPL software library is used for purposes of robot path planning. Experimental results for different planning algorithms with various static and dynamic obstacles are shown. Comparison between created method and method without path replanning is made. The method is tested on a real collaborative application to evaluate its performance and versatility.

The aim of this work is to design a robotic bioprinting platform able to fabricate a three-dimensional structure onto irregular surfaces. With respect to the limitations of current in vitro bioprinting approach, widely used in scaffold-based tissue engineering – handling difficulty, risk of contamination, shape not matching with the defect site – this robotic bioprinter can offer an innovative solution allowing in situ bioprinting, a direct dispensing of biological materials onto and into the damaged site. The robotic platform was developed starting from the 5 degrees-of-freedom open source MOVEO robot from BCN3D. The hardware and the software of the original project were re-engineered to control the robot using LinuxCNC, a path planning algorithm was developed in Matlab®, and the end-effector was equipped with a pneumatic extruder. The algorithm automatically projects any generic printing pattern on the surface on which the scaffold will be 3D bioprinted. For each point, the algorithm calculates the joint angles to keep the end effector always perpendicular to the surface. A g-code file is then exported to Linux CNC adding parameters to control the air pressure and the printing speed. The robotic platform was tested to evaluate its performances. Resolution (~200 ​μm) and repeatability were estimated and preliminary in situ bioprinting tests were performed onto different irregular surfaces, including a physiologically relevant bone model.

In the operation of a broad range of industrial processes, the uses of robots whose trajectories are constrained by the velocity of the parts actuated (tools, end effectors, joints and the like) more often than not play a significant role. Keeping our focus on systems for material deposition such as painting, glueing, aerosol spraying but nowadays also additive manufacturing techniques like FDM processes, it is noticeable that a key parameter is the control of the flow of material in accordance with the trajectory velocity of the parts being actuated. According to the specific requirements and goals of different technology, it is possible to generate different trajectories. In this paper, we propose an original path planning algorithm based on the use of Bezier curves aimed at assuring regulation of the velocity and a uniform distribution of the extruded material referring to an innovative additive manufacturing technology. In particular, the paper presents a path planning technology developed for an application where it is necessary to maintain a constant velocity along the length of the trajectory aimed at improving the technological processes on the basis of an innovative additive technology. The paper further presents a working application with a machine prototype so as to demonstrate the viability and performance of the work under consideration.

Z razvojem avtomatsko vodenih vozil (AGV) in avtomatiziranih skladišč se je razvila potreba po razvoju algoritmov za avtonomno planiranje poti in tudi avtonomnem planiranju poti za parkiranje. Eden od ključnih problemov pri procesu parkiranja je planiranje poti. V tej študiji bo prikazano reševanje problema poti parkiranja štirikolesnega vozila AGV. V teoretičnem delu so predstavljeni trije algoritmi za planiranje poti: Dubinsove poti, RRT (ang. Rapidly-exploring random tree) algoritem in algoritem za iskanje poti s pomočjo krožnih lokov. V praktičnem delu je narejena študija algoritma za iskanje poti s pomočjo krožnih lokov, sam algoritem je implementiran in testiran v simulacijskem okolju. Glavni poudarek je na testiranju delovanja tega algoritma in predstavitvi rezultatov simulacije. Pot, ki povezuje začetno pozicijo AGV s končno pozicijo je sestavljena iz krožnih lokov. Algoritem je testiran v simulacijskem okolju, kjer so dimenzije parkirnega prostora in AGV-ja v naprej določene. Na začetku algoritem najde več kot eno končno krožnico, ki povezuje začetno in končno pozicijo AGV-ja. Krožnica, ki je izbrana kot končna izpolnjuje dva pogoja: radij končnega kroga mora bit večji od radija obračanja (Rturn) AGV-ja in tangentna točka med končno krožnico in krožnico obračanja AGV-ja mora bit nad X-osjo lokalnega koordinatnega sistema. Predlagani algoritem zagotavlja praktično metodo načrtovanja poti brez računsko zamudnega računanja, prav tako pa je sposoben najti končno pot iz katerekoli začetne pozicije AGV-ja. V tej študiji je bilo pokazano, da se dolžina poti spreminja s spreminjanjem naslednjih parametrov: pozicija in orientacija AGV-ja, krmilni kot, parkiranje naprej ali vzvratno. Narejeni so bili štirje optimizacijski algoritmi. Prvi dva algoritma optimirata pozicijo in orientacijo AGV-ja, če poznamo njegov krmilni kot theta. S to optimizacijo dobimo najboljše območje, kjer se lahko parkirni proces začne in tudi najboljšo orientacijo AGV-ja za parkiranje. Tretji algoritem optimira krmilni kot theta, kjer imamo podano začetno pozicijo in orientacijo AGV-ja. S to optimizacijo dobimo krmilni kot, s katerim pridemo do najbolj optimalne (najkrajše) poti. Pri zadnji optimizaciji se preveri način parkiranja (naprej ali vzvratno) s katerim pridemo do najbolj optimalne (najkrajše) poti. S pomočjo vseh optimizacij je bilo ugotovljeno, da je pot najkrajša, ko je začetna pozicija AGV-ja tudi tangenta točka med končno krožnico in krožnico obračanja AGV-ja. Dobra stran algoritma je, da vedno najde pot. Z uporabo različnih vrst optimizacije lahko pot prilagodimo glede na potrebe uporabnika (optimalno pot, ki pomeni najkrajšo pot, ali pot z določenim številom manevrov ali druge).Nadaljnji razvoj algoritma bi lahko vseboval testiranje na realnem primeru v realnem okolju. A path planning algorithm for parking an autonomous vehicle. Recently, with the development of self-driving, autonomous vehicles and automated warehouses there have been a growing interest in the development of autonomous path planning as well as parking algorithms. In this study, a solution for path planning in autonomous parking for 4-wheel-AGV(Automated Guided Vehicle) is presented. In the theoretic part of the study 3 path planning algorithms are exposed such as: Dubbin’s path, RRT (Rapidly-exploring random tree) algorithm and an arc path planning algorithm. In the practical part of the study the arc path parking algorithm is implemented and tasted. The main focus is given to algorithm testing and presentation of obtained test results in simulation environment. The path is circle arc that connects the vehicle initial position and goal position of the vehicle inside the parking space. The algorithm is tested in a simulation environment where parking space and dimension of the vehicle are known. At the beginning from the initial position of the vehicle to the final position the algorithm find more than one potential final circle that can be used to defending the final arc path. But the circle that is picked as final path has to have radius larger than the turning radius of the vehicle and the tangent point between the potential final circle and circles with turning radius of the car have to be above X axis of the local coordinate system. The proposed path planning algorithm provides a practical method of path planning without expensive computation and it was proved that if find a final path from any initial position. In the study it was proved that the length of the final path in changing depending on this parameters: position in orientation of the vehicle, steering angle and parking forward/backward. In the study 4 different optimizations to find the shortest path were done so the optimal path can be found. First one is optimization of the positions and orientations of the vehicle where we know the steering angle thata. With this optimization the best area where the vehicle should start with the parking procedure and path planning and the best orientation of the vehicle are found. Third is optimization of the steering angle theta, where we have the initial starting position and orientation of the vehicle. With this optimization we get what is the best steering angle of the vehicle so we get the optimal path length. And the last optimization is with parking forward/backward. With the optimization it was shown that the length of the path is shortest when vehicle park forward if it is turn with the front side of it to the parking spot. And if it park backward it is better if it is turned with the back towards the parking spot. Also while doing all the optimizations it was shown that the shortest path is found if the tangent point between final circle and circle with turning radius is same as the position point of the vehicle. The good side of the algorithm is that it always find a path, without expensive computations. But the path might not be always optimal, that is why it is good to know the optimization parameter that is important. If it is searching for optimal path meaning the shortest path, or the path with defined number of maneuvers or other optimization. The further work on the algorithm would be implementing and testing in on a real example in real environment.

In this thesis, an exploration mode for the PUREi9 robotic vacuum cleaner is implemented. This exploration would provide information for optimizing the cleaning path beforehand, and would allow the robot to relocalize itself or the charger more easily in case it gets lost. Two elements are needed in order to implement an exploration mode; first, an exploration algo-rithm which will decide the next position of the robot in order to obtain useful information about the environment (unknown areas, empty spaces, obstacles...), and second, an exploration map which stores that information and is updated each time a new relevant position is reached. These elements are related and generally both are required for performing successfully the exploration of a specific environment. A frontier-based strategy is adopted for the exploration algorithm, together with occupancy grid maps. This strategy has long been regarded as a key method for autonomous robots working in unknown or changing environments. The idea of frontier-based algorithms is to divide the environ-ment into cells of regular size and drive the robot to the frontiers between cells with no obstacles and cells for which no information has been gathered. It plans one step ahead by choosing a lo-cation which provides new environment information, instead of planning in advance all locations where the robot needs to acquire new sensor information. Based on frontier strategy, two different exploration algorithms are implemented in the project. The first one is called "random frontier strategy", which chooses arbitrarily the frontier to go among the frontiers set. The second is called "closest frontier strategy", which chooses the closest frontier as the NBV (Next Best View) the robot should drive to. A path planning algorithm, based on Dijkstra’s algorithm and a node graph, has also been implemented in order to guide the robot towards the frontiers. The two methods have been compared by means of simulations in different environments. In addition, both exploration strategies have been tested on a real device. It is found that the closest frontier strategy is more efficient in terms of path length between scanning points, while both methods give a similar exploration ratio, or percentage of fully explored cells within the final map. Some additional work is required in order to improve the performance of the exploration method in the future, such as detecting unreachable frontiers, implementing a more robust path planning algorithm, or filtering the laser measurements more extensively. I den här rapporten har vi implementerat en utforskningsmod för robotdammsugaren Pure i9. Sådan utforskning skulle ge underlag för att optimera städmönstret i förhand och låta roboten relokalisera sig själv eller laddaren om den tappar bort sig. För att implementera utforskning behövs två saker. För det första krävs en algoritm för utforsk-ning, som bestämmer nästa position för roboten, med målet att samla användbar information om omgivningen (okända eller fria områden, hinder etc.) För det andra krävs en karta som lagrar informationen och uppdateras varje gång roboten når en relevant ny position. Dessa två hänger ihop och i allmänhet krävs båda för att framgångsrikt utforska ett specifikt område. Vi har valt en front-baserad strategi för utforskningsalgoritmen, tillsammans med en rutnäts-karta med sannolikheten för hinder. Denna strategi har länge betraktats som en nyckelmetod för autonoma robotar som arbetar i okända eller föränderliga miljöer. Idén med front-baserade strate-gier är att köra roboten till fronterna mellan celler utan hinder och celler där information saknas. Den planerar ett steg framåt genom att välja en plats som ger ny information om miljön, istället för att i förväg planera alla platser där roboten behöver samla in ny sensorinformation. Baserat på front-strategi, har vi implementerat två utforskningsalgoritmer i projektet. Den första är en slumpmässig strategi, som godtyckligt väljer en front att åka till, ur hela mängden av fronter. Den andra är en närmaste fronten-strategi som väljer den närmaste fronten som den nästa bästa utsiktspunkt som roboten ska åka till. Vi har också implementerat en algoritm för banplanering, baserad på Dijkstras algoritm och en nod-graf, för att styra roboten mot fronterna. Vi har jämfört de två metoderna genom simulering i olika miljöer. Dessutom har båda utforsk-ningsstrategierna testats på en riktig enhet. Närmaste fronten-strategin är effektivare med avse-ende på banlängd mellan skanningspunkter, medan båda metoderna ger liknande utforsknings-grad, eller samma procentandel av fullt utforskade celler inom den slutliga kartan.

In Wireless Sensors Networks (WSNs), researcher’s main focus is on energy preservation and prolonging network lifetime. More energy resources are required in case of remote applications of WSNs, where some of the nodes die early that shorten the lifetime and decrease the stability of the network. It is mainly caused due to the non-optimal Cluster Heads (CHs) selection based on single criterion and uneven distribution of energy. We propose a new clustering protocol for both homogeneous and heterogeneous environments, named as Optimized Path planning algorithm with Energy efficiency and Extending Network lifetime in WSN (OPEN). In the proposed protocol, timer value concept is used for CH selection based on multiple criteria. Simulation results prove that OPEN performs better than the existing protocols in terms of the network lifetime, throughput and stability. The results explicitly explain the cluster head selection of OPEN protocol and efficient solution of uneven energy distribution problem.

© 2013 by MDPI (http://www.mdpi.org). Reproduction is permitted for noncommercial purposes., Low-cost robots are characterized by low computational resources and limited energy supply. Path planning algorithms aim to find the optimal path between two points so the robot consumes as little energy as possible. However, these algorithms were not developed considering computational limitations (i.e., processing and memory capacity). This paper presents the HCTNav path-planning algorithm (HCTLab research group’s navigation algorithm). This algorithm was designed to be run in low-cost robots for indoor navigation. The results of the comparison between HCTNav and the Dijkstra’s algorithms show that HCTNav’s memory peak is nine times lower than Dijkstra’s in maps with more than 150,000 cells., This work has been partially supported by the Spanish “Ministerio de Ciencia e Innovación”, under project TEC2009-09871.

This thesis presents a computational model of the surgical stitching tasks and a path planning algorithm for robotic assisted stitching. The overall goal of the research is to enable surgical robots to perform automatic suturing. Suturing comprises several distinct steps, one of them is the stitching. During stitching, reaching the desired exit point is difficult because it must be accomplished without direct visual feedback. Moreover, the stitching is a time consuming procedure repeated multiple times during suturing. Therefore, it would be desirable to enhance the surgical robots with the ability of performing automatic suturing. The focus of this work is on the automation of the stitching task. The thesis presents a model based path planning algorithm for the autonomous stitching. The method uses a nonlinear model for the curved needle - soft tissue interaction. The tissue is modeled as a deformable object using continuum mechanics tools. This thesis uses a mesh free deformable tissue model namely, Reproducing Kernel Particle Method (RKPM). RKPM was chosen as it has been proven to accurately handle large deformation and requires no re-meshing algorithms. This method has the potential to be more realistic in modeling various material characteristics by using appropriate strain energy functions. The stitching task is simulated using a constrained deformable model; the deformable tissue is constrained by the interaction with the curved needle. The stitching model was used for needle trajectory path planning during stitching. This new path planning algorithm for the robotic stitching was developed, implemented, and evaluated. Several simulations and experiments were conducted. The first group of simulations comprised random insertions from different insertion points without planning to assess the modeling method and the trajectory of the needle inside the tissue. Then the parameters of the simulations were set according to the measured experimental parameters. The proposed path planning method was tested using a surgical ETHICON needle of type SH 1=2 Circle with the radius of 8:88mm attached to a robotic manipulator. The needle was held by a grasper which is attached to the robotic arm. The experimental results illustrate that the path planned curved needle insertions are fifty percent more accurate than the unplanned ones. The results also show that this open loop approach is sensitive to model parameters. Master of Applied Science (MASc)

This paper presents a new algorithm for path planning in a time-varying environment based on graphical methods. The methods presented make it possible to find an optimal path using defined requirements in a feasible time. The task of the introduced path planning algorithm using an AUV is to find a time-optimal path from a defined start position to a goal position by evasion of all static as well as dynamic obstacles in the area of operation, with consideration of the dynamic vehicle behaviour and the time-varying ocean current. An additional consideration is to show whether it is feasible to use the algorithms on-line and on-board a Webb “SLOCUM” glider AUV. The presented algorithm is equally applicable to land based or aerial mobile autonomous systems., 16th International Symposium on Unmanned Untethered Submersible Technology (UUST'09), 23-26 August 2009, Durham, New Hampshire