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نتیجه جستجو - برنامه ریزی مسیر

تعداد مقالات یافته شده: 14
ردیف عنوان نوع
1 Complete coverage path planning using reinforcement learning for Tetromino based cleaning and maintenance robot
برنامه ریزی کامل مسیر پوشش با استفاده از یادگیری تقویتی برای تمیز کاری و نگهداری ربات مبتنی بر Tetromino-2020
Tiling robotics have been deployed in autonomous complete area coverage tasks such as floor cleaning, building inspection, and maintenance, surface painting. One class of tiling robotics, polyomino-based reconfigurable robots, overcome the limitation of fixed-form robots in achieving high-efficiency area coverage by adopting different morphologies to suit the needs of the current environment. Since the reconfigurable actions of these robots are produced by real-time intelligent decisions during operations, an optimal path planning algorithm is paramount to maximize the area coverage while minimizing the energy consumed by these robots. This paper proposes a complete coverage path planning (CCPP) model trained using deep blackreinforcement learning (RL) for the tetromino based reconfigurable robot platform called hTetro to simultaneously generate the optimal set of shapes for any pretrained arbitrary environment shape with a trajectory that has the least overall cost. To this end, a Convolutional Neural Network (CNN) with Long Short Term Memory (LSTM) layers is trained using Actor Critic Experience Replay (ACER) reinforcement learning algorithm. The results are compared with existing approaches which are based on the traditional tiling theory model, including zigzag, spiral, and greedy search schemes. The model is also compared with the Travelling salesman problem (TSP) based Genetic Algorithm (GA) and Ant Colony Optimization (ACO) schemes. The proposed scheme generates a path with lower cost while also requiring lesser time to generate it. The model is also highly robust and can generate a path in any pretrained arbitrary environments.
Keywords: Tiling robotics | Cleaning and maintenance | Inspection | Path planing | Reinforcement learning
مقاله انگلیسی
2 Reinforcement learning in dual-arm trajectory planning for a free-floating space robot
یادگیری تقویتی در برنامه ریزی مسیر دو بازو برای یک ربات فضایی شناور آزاد-2020
A free-floating space robot exhibits strong dynamic coupling between the arm and the base, and the resulting position of the end of the arm depends not only on the joint angles but also on the state of the base. Dynamic modeling is complicated for multiple degree of freedom (DOF) manipulators, especially for a space robot with two arms. Therefore, the trajectories are typically planned offline and tracked online. However, this approach is not suitable if the target has relative motion with respect to the servicing space robot. To handle this issue, a model-free reinforcement learning strategy is proposed for training a policy for online trajectory planning without establishing the dynamic and kinematic models of the space robot. The model-free learning algorithm learns a policy that maps states to actions via trial and error in a simulation environment. With the learned policy, which is represented by a feedforward neural network with 2 hidden layers, the space robot can schedule and perform actions quickly and can be implemented for real-time applications. The feasibility of the trained policy is demonstrated for both fixed and moving targets.
Keywords: On-orbit servicing | Free-floating space robot | Dual-arm trajectory planning | Reinforcement learning | Fixed and moving targets
مقاله انگلیسی
3 A novel reinforcement learning based grey wolf optimizer algorithm for unmanned aerial vehicles (UAVs) path planning
الگوریتم جدید بهینه سازی یادگیری تقویتی گرگ خاکستری برای برنامه ریزی مسیر وسایل نقلیه هوایی بدون سرنشین (پهپاد)-2020
Unmanned aerial vehicles (UAVs) have been used in wide range of areas, and a high-quality path planning method is needed for UAVs to satisfy their applications. However, many algorithms reported in the literature may not feasible or efficient, especially in the face of three-dimensional complex flight environment. In this paper, a novel reinforcement learning based grey wolf optimizer algorithm called RLGWO has been presented for solving this problem. In the proposed algorithm, the reinforcement learning is inserted that the individual is controlled to switch operations adaptively according to the accumulated performance. Considering that the proposed algorithm is designed to serve for UAVs path planning, four operations have been introduced for each individual: exploration, exploitation, geometric adjustment, and optimal adjustment. In addition, the cubic B-spline curve is used to smooth the generated flight route and make the planning path be suitable for the UAVs. The simulation experimental results show that the RLGWO algorithm can acquire a feasible and effective route successfully in complicated environment.
Keywords: Unmanned aerial vehicles (UAVs) | Three-dimensional path planning | Reinforcement learning | Grey wolf optimizer
مقاله انگلیسی
4 Path planning for asteroid hopping rovers with pre-trained deep reinforcement learning architectures
برنامه ریزی مسیر برای گام های مریخ نورد با معماری یادگیری تقویتی عمیق از پیش اموزش دیده -2020
Asteroid surface exploration is challenging due to complex terrain topology and irregular gravity field. A hopping rover is considered as a promising mobility solution to explore the surface of small celestial bodies. Conventional path planning tasks, such as traversing a given map to reach a known target, may become particularly challenging for hopping rovers if the terrain displays sufficiently complex 3-D structures. As an alternative to traditional path-planning approaches, this work explores the possibility of applying deep reinforcement learning (DRL) to plan the path of a hopping rover across a highly irregular surface. The 3-D terrain of the asteroid surface is converted into a level matrix, which is used as an input of the reinforcement learning algorithm. A deep reinforcement learning architecture with good convergence and stability properties is presented to solve the rover path-planning problem. Numerical simulations are performed to validate the effectiveness and robustness of the proposed method with applications to two different types of 3-D terrains.
Keywords: Asteroid surface exploration | Hopping rover | Path planning | Deep reinforcement learning
مقاله انگلیسی
5 A hybrid route planning approach for logistics with pickup and delivery
یک رویکرد برنامه ریزی مسیر ترکیبی برای تدارکات با پیکاپ و تحویل-2019
With the busy life of modern people, more and more consumers are preferring to shop online. This change on shopping behavior results in large volumes of packages must be transported, and thus re- search on logistics planning considering real constraints has increased. To solve this problem, several heuristics or evolutionary methods with expert knowledge were proposed previously, but they are usu- ally inefficient or need a large amount of memory. In this paper, we propose a hybrid approach called Iterative Logistics Solution Planner ( ILSP ) for not only quickly finding a nice logistics solution but also itera- tively improving the solution quality while meeting the real logistics constraints. ILSP contains two main phases including initial logistics solution generation and iterative logistics solution improvement based on the intelligence and knowledge from domain experts. Several algorithms and strategies are designed in ILSP for package partitioning, route planning and quality improvement. From the view of expert sys- tems, the significance and impact of ILSP are simultaneously taking both computational efficiency and iterative quality improvement based on the expert knowledge into account on logistics planning problem with pickup and delivery. Through the rigorous experimental evaluations of real logistics data, the results demonstrated the excellent performance of ILSP .
Keywords: Hybrid approach | Logistics planning | Smart city| Expert system
مقاله انگلیسی
6 Success history applied to expert system for underwater glider path planning using differential evolution
تاریخچه موفقیت برای برنامه ریزی مسیر گلایدر در زیر آب با استفاده از تکامل افتراقی برای سیستم خبره کاربردی-2019
This paper presents an application of a recently well performing evolutionary algorithm for continuous numerical optimization, Success-History Based Adaptive Differential Evolution Algorithm (SHADE) includ- ing Linear population size reduction (L-SHADE), to an expert system for underwater glider path planning (UGPP). The proposed algorithm is compared to other similar algorithms and also to results from lit- erature. The motivation of this work is to provide an alternative to the current glider mission control systems, that are based mostly on multidisciplinary human-expert teams from robotic and oceanographic areas. Initially configured as a decision-support expert system, the natural evolution of the tool is target- ing higher autonomy levels. To assess the performance of the applied optimizers, the test functions for UGPP are utilized as defined in literature, which simulate real-life oceanic mission scenarios. Based on these test functions, in this paper, the performance of the proposed application of L-SHADE to UGPP is aggregated using statistical analyis. The depicted fitness convergence graphs, final obtained fitness plots, trajectories drawn, and per-scenario analysis show that the new proposed algorithm yields stable and competitive output trajectories. Over the set of benchmark missions, the newly obtained results with a configured L-SHADE outperforms ex- isting literature results in UGPP and ranks best over the compared algorithms. Moreover, some additional previously applied algorithms have been reconfigured to yield improved performance. Thereby, this new application of evolutionary algorithms to UGPP contributes significantly to the capacity of the decision- makers, when they use the improved UGPP expert system yielding better trajectories.
Keywords: Differential evolution | Linear population size reduction | Success-history based parameter adaptation | L-SHADE | Underwater glider path planning
مقاله انگلیسی
7 Ontology based knowledge representation technique, domain modeling languages and planners for robotic path planning: A survey
تکنیک بازنویسی دانش هستی شناسی، مدل سازی دامنه زبان ها و برنامه ریزان برای برنامه ریزی مسیر رباتیک: یک مرور-2018
Knowledge Representation and Reasoning (KR & R) has become one of the promising fields of Artificial Intelligence. KR is dedicated towards representing information about the domain that can be utilized in path planning. Ontology based knowledge representation and reasoning techniques provide sophisticated knowledge about the environment for processing tasks or methods. Ontology helps in representing the knowledge about environment, events and actions that help in path planning and making robots more autonomous. Knowledge reasoning techniques can infer new conclusion and thus aids planning dynamically in a non-deterministic environment. In the initial sections, the representation of knowledge using ontology and the techniques for reasoning that could contribute in path planning are discussed in detail. In the following section, we also provide comparison of various planning domain modeling languages, ontology editors, planners and robot simulation tools.
Keywords: Path planning; Knowledge representation; Reasoning; Ontology; Spatial; Temporal; Semantic knowledge; Planners; Modeling languages
مقاله انگلیسی
8 A diffusion planning mechanism for social marketing
مکانیسم برنامه ریزی پخش برای بازاریابی اجتماعی-2017
Social media is gaining importance as a component of marketing strategies. Many types of social media, such as social networking sites, blogospheres and micro-blogospheres, have been seeking business opportunities and establishing brand expression in the recent years. Online marketing information diffusion has become the critical business model of online social networks. However, most of the current marketing studies on discovering potential influences do not appropriately support them to diffuse advertisements. Therefore, marketing information may be lost during the diffusion process and cannot be sent to potential customers successfully. In this research, a diffusion path planning mechanism for advertisements is developed to help influencers to propagate marketing information and help marketers to evaluate possible rewards under different marketing strategies. Our experimental results show that the proposed mechanism can significantly improve the diffusion process of advertising messages and decrease marketing uncertainty.
Keywords: Information diffusion | Social network | Social media marketing | Path planning | Influential nodes
مقاله انگلیسی
9 Geometric backtracking for combined task and motion planning in robotic systems
بازخوانی هندسی برای کارهای ترکیبی و برنامه ریزی حرکت در سیستم های رباتیک-2017
Article history:Received in revised form 10 February 2015 Accepted 21 March 2015Available online 14 May 2015Keywords:Combined task and motion planning Task planningAction planning Path planning RoboticsGeometric reasoning Hybrid reasoning Robot manipulationPlanners for real robotic systems should not only reason about abstract actions, but also about aspects related to physical execution such as kinematics and geometry. We present an approach to hybrid task and motion planning, in which state-based forward-chaining task planning is tightly coupled with motion planning and other forms of geometric reasoning. Our approach is centered around the problem of geometric backtracking that arises in hybrid task and motion planning: in order to satisfy the geometric preconditions of the current action, a planner may need to reconsider geometric choices, such as grasps and poses, that were made for previous actions. Geometric backtracking is a necessary condition for completeness, but it may lead to a dramatic computational explosion due to the large size of the space of geometric states. We explore two avenues to deal with this issue: the use of heuristics based on different geometric conditions to guide the search, and the use of geometric constraints to prune the search space. We empirically evaluate these different approaches, and demonstrate that they improve the performance of hybrid task and motion planning. We demonstrate our hybrid planning approach in two domains: a real, humanoid robotic platform, the DLR Justin robot, performing object manipulation tasks; and a simulated autonomous forklift operating in a warehouse. 2015 Elsevier B.V. All rights reserved.
Keywords:Combined task and motion planning | Task planning | Action planning | Path planning | Robotics | Geometric reasoning | Hybrid reasoning | Robot manipulation
مقاله انگلیسی
10 Planning for tourism routes using social networks
برنامه ریزی مسیرهای گردشگری با استفاده از شبکه های اجتماعی-2017
Traveling recommendation systems have become very popular applications for organizing and planning tourist trips. Among other challenges, these applications are faced with the task of maintaining updated information about popular tourist destinations, as well as providing useful tourist guides that meet the users preferences. In this work we present the PlanTour, a system that creates personalized tourist plans using the human-generated information gathered from the minube1 traveling social network. The system follows an automated planning approach to generate a multiple-day plan with the most relevant points of interest of the city/region being visited. Particularly, the system collects information of users and points of interest from minube, groups these points with clustering techniques to split the problem into per day sub-problems. Then, it uses an off-the-shelf domain-independent automated planner that finds good quality tourist plans. Unlike other tourist recommender systems, the PlanTour planner is able to organize relevant points of interest taking into account user’s expected drives, and user scores from a real social network. The paper also highlights how to use human provided recommendations to guide the search for solutions of combinatorial tasks. The resulting intelligent system opens new possibilities of combin ing human-generated knowledge with efficient automated techniques when solving hard computational tasks. From an engineering perspective we advocate for the use of declarative representations of problem solving tasks that have been shown to improve modeling and maintenance of intelligent systems.
Keywords: Tourism routes | Automated planning | Recommender systems
مقاله انگلیسی
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