Since their introduction in the 1950s, industrial Automated Guided Vehicles (AGV) have gone from automatic machinery limited by hardware to complex robots limited by software, called Autonomous Mobile Robots. Small and medium businesses need to be able to utilize cutting-edge technology. Therefore, this research focuses on deploying mapless AMRs on cheap open source AMRs in dynamic industrial environments. The study implements Dijkstra’s and A-STAR algorithms on a simulated Turtlebot3 model deployed in a Gazebo rendering of an industrial warehouse with moving objects added. The Turtlebot3 model traverses the environment where time and distance results are observed. The results shown in the research indicate that Dijkstra’s algorithm is barely affected by the change of the initial map state, while the A-STAR algorithm performed worse on average. Future work should focus on minimizing the sensors needed and continue testing with more algorithms, but early tests show promising results.

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I denna uppsats presenteras resultatet av ett examensarbete i maskinkonstruktion på KTH. Arbetet har utförts på uppdrag av Poki Robotics via konsultbolaget Omecon.Poki Robotics vill automatisera uppsamlingen av golfbollar på så kallade driving ranges, genom att nyttja en hjuldriven autonom robot med ett tillhörande uppsamlingssläp. Enligt Poki Robotics samlas 5000 golfbollar upp under en intensiv dag, vilket innebär att en total massa av ca 230 kg måste uppsamlas och förflyttas. Detta ställer höga krav på dragkraften som drivlinan måste förse roboten med.Rapporten beskriver konceptframtagningen av drivlinan till den hjuldrivna autonoma roboten. Konceptet genererades genom att först utvärdera framdrivning inom robotik i en förstudie. Olika styrsystem utvärderades utifrån ställda krav på mjukvarubaserad styrning, mekanisk komplexitet, effektivitet och mobilitet. Styrsystemen utvärderades i en Pugh-matris som mynnade ut i två styrningar som visades mest lovande. Styrningarna utvärderades med en terrängmekanisk beräkningsmodell för att utvärdera framdrivande egenskaper, och därmed bedöma vilken som var mest lämpad för syftet utifrån ställda krav.För den lämpade styrningen genererades och utvärderades koncept för drivlinan med avseende på underhåll, montage, tillverkning, robusthet och förmåga att uppta laster. Det slutgiltiga konceptet bestod av en borstlös likströmsmotor, försedd med ett planetväxelhuvud och en parallell hjulaxel med en kuggremsutväxling.Konceptframtagningen resulterade i en CAD-modell som visade att drivlinan uppfyllde dimensionella krav.Den terrängmekaniska beräkningsmodellen nyttjades enbart som ett verktyg för att ge en indikation på prestanda och som indata för konstruktionen. Beräkningsmodellen har begränsningar i form av att den inte är lämpad för små hjuldiametrar, att den inte tar hänsyn till rotförstärkt mark samt att den utesluter de pneumatiska däckens elastiska egenskaper. / This thesis presents the result of a master thesis in machine design at KTH. The task was performed for Poki Robotics via the consulting firm Omecon.Poki Robotics wants to generate an autonomous solution for the collecting of golf balls on driving ranges, by utilizing a wheeled autonomous mobile robot coupled with a towed collecting unit. According to Poki Robotics, 5000 golf balls are collected on a busy day, yielding a total mass of approximately 230 kg that must be collected and towed. This sets high demands on the towing force that needs to be provided by the powertrain.The report describes the concept generation of the powertrain for a wheeled autonomous robot. A prestudy was done to evaluate wheeled locomotion in mobile robotics. Different steering systems were evaluated by different metrics including mechanical complexity, efficiency, mobility and demand for software-based control. The steering systems were evaluated using Pugh matrices, yielding two steering systems that showed to be promising. A terramechanical analytical model was used to further evaluate tractive performance of the two steering systems, to conclude which steering system was most suitable for the purpose with respect to the set requirements.Concepts were generated and evaluated for the powertrain of the chosen steering system, with respect to maintenance, assembly, manufacturing, robustness and load carrying capacity. The final concept yielded an EC-motor with a planetary gearhead, with a parallel wheel shaft and a timing belt gearing.The concept generation resulted in a CAD-model showing that the powertrain met the targeted dimensional constraints.The terramechanical analytical modelling was used solely as a tool for indication of performance and as input data for design. The model has limitations due to it not being suitable for small wheel diameters, not taking root reinforcement of the soil into account and excluding the pneumatic tire’s elastic properties.

京都大学 / 新制・課程博士 / 博士(工学) / 甲第23884号 / 工博第4971号 / 新制||工||1776(附属図書館) / 京都大学大学院工学研究科機械理工学専攻 / (主査)教授 小森 雅晴, 教授 松野 文俊, 教授 藤本 健治 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Yes / This paper proposes a novel trajectory tracking control approach for nonholonomic wheeled mobile robots. In this approach, the integration of feed-forward and feedback controls is presented to design the kinematic controller of wheeled mobile robots, where the control law is constructed on the basis of Lyapunov stability theory, for generating the precisely desired velocity as the input of the dynamic model of wheeled mobile robots; a proportional-integral-derivative based membrane controller is introduced to design the dynamic controller of wheeled mobile robots to make the actual velocity follow the desired velocity command. The proposed approach is defined by using an enzymatic numerical membrane system to integrate two proportional-integral-derivative controllers, where neural networks and experts’ knowledge are applied to tune parameters. Extensive experiments conducted on the simulated wheeled mobile robots show the effectiveness of this approach. / The work of XW and GZ is supported by the National Natural Science Foundation of China (61170016, 61373047). The work of MG, FI and RL was supported by a grant of the Romanian National Authority for Scientific Research, CNCS-UEFISCDI (project number: PN-II-ID-PCE-2011-3-0688).

Yes / To solve the multi-objective mobile robot path planning in a dangerous environment with dynamic obstacles, this paper proposes a modified membraneinspired algorithm based on particle swarm optimization (mMPSO), which combines membrane systems with particle swarm optimization. In mMPSO, a dynamic double one-level membrane structure is introduced to arrange the particles with various dimensions and perform the communications between particles in different membranes; a point repair algorithm is presented to change an infeasible path into a feasible path; a smoothness algorithm is proposed to remove the redundant information of a feasible path; inspired by the idea of tightening the fishing line, a moving direction adjustment for each node of a path is introduced to enhance the algorithm performance. Extensive experiments conducted in different environments with three kinds of grid models and five kinds of obstacles show the effectiveness and practicality of mMPSO. / National Natural Science Foundation of China (61170016, 61373047), the Program for New Century Excellent Talents in University (NCET-11-0715) and SWJTU supported project (SWJTU12CX008); grant of the Romanian National Authority for Scientific Research, CNCSUEFISCDI, project number PN-II-ID-PCE- 2011-3-0688.

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Navigation is the fundamental capability of mobile robots which allows them to move fromone point to another without any human interference. The autonomous operation of theserobots is depended on reliable, robust, and intelligent navigation system. With the recenttechnological progress, autonomous mobile robots are being deployed and used in differentareas and scenarios. Conventional navigation approaches depend on predefined accurateobstacle maps and costly high-end precise laser sensors. These maps are difficult and expensiveto acquire and degrade due changes in the environment. This limits the overall use of mobilerobots in dynamic settings. In this research, we investigate the end-to-end learning-basedapproach using vision and ranging sensors while using Deep Reinforcement Learning formobile robot navigation for indoor environments. Different state-of-the-art DRL algorithms were trained and compared in 3D-simulation in termsof sample efficiency and cumulative reward. Next, extensive experiments were carried outusing 10-dimensional sparse distance data from vision and ranging sensor. The trained modelswere evaluated in different environments of varying complexity to analyze the strength andgeneralizability of the learnt policies. Our results showed that ranging sensor approach was able to learn a robust navigation policywhich was able to generalize in unseen virtual environments without any additional trainingwith a high success rate. Whereas vision-based approach performed poorly due to insufficientinformation and hardware constraints. Moreover, all the experiment were carried out only insimulation. However, they should be directly transferable to an actual robot since abstractobservation space was used.

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