Traditional Planning Methods Research Articles

Hierarchical multi-agent planning for flexible assembly of large-scale lunar facilities

Several national space agencies and commercial aerospace companies plan to set up lunar bases with large-scale facilities that rely on multiple lunar robots’ assembly. Mission planning is necessary to achieve efficient multi-robot cooperation. This paper aims at autonomous multi-robot planning for the flexible assembly of the large-scale lunar facility, considering the harsh lunar environment, mission time optimization, and joint actions. The lunar robots and modules are scattered around the mission area without fixed assembly lines. Thus, the traditional assembly planning methods ignoring the optimal selection of modules are unable to handle this problem. We propose a hierarchical multi-agent planning method based on two-stage two-sided matching (HMAP-TTM) to solve this critical problem. First, the distributed planning framework with multi-replica public agents is introduced, ensuring robot plan knowledge consistency through public agents’ communication. Second, the hierarchical task graph (HTG) divides the mission into task layers based on task dependency knowledge. Third, we develop a novel two-stage two-sided matching algorithm. Time-optimal plans emerge from the matching games among public and private agents in each layer of HTG. Agents make decisions in the game based on action knowledge updated during planning. Finally, an assembly mission is presented to prove the method’s effectiveness. The simulation results show that the HMAP-TTM can generate plans with shorter mission time and require smaller communication costs than the baseline methods.

Space Manipulator Assembly Operation Technique based on Deep Residual Reinforcement Learning

In recent years, there are more and more space complex operational tasks such as the maintenance and assembly of on-orbit aircraft. Traditional robot planning and control methods require precise dynamic models, which are difficult to accommodate to on-orbit assembly operations in extreme space environments. Typical space operation tasks, such as plug and pull operation, whose control strategy can be artificially designed. Being artificially designed by combining the output control strategy and deep reinforcement learning algorithm, which can simplify the training difficulty of deep reinforcement learning, making the learning process more efficient and training results better. In this paper, a deep Residual reinforcement learning algorithm combined with a heuristic control strategy is constructed to complete the space mechanical arm assembly operation training in a highly realistic simulation environment. Based on the experimental data, the Residual deep reinforcement-learning algorithm designed in this paper shows the performance of rapid convergence and can complete the on-orbit assembly operation task with a high probability.

A Non-Rigid Hierarchical Discrete Grid Structure and Its Application to UAVs Conflict Detection and Path Planning

<p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Following the development of the unmanned aerial vehicle (UAV) industry, the number of UAVs has increased significantly, and higher expectations regarding the effect of spatial path planning have been expressed. Traditional coordinate-based conflict detection and path planning methods of UAVs are highly complex and have low computational efficiency. While the conflict detection method based on the grid system can improve efficiency, the rigid grid structure of the existing local grid system or discrete global grid system is insufficient. Since the rigid structure only encodes grid cells, multiple or different size grids are required to identify UAVs in motion, further complicating path planning. The main contributions of this article are as follows. First, we proposed a nonrigid hierarchical discrete grid structure and coding method for spatial three-dimensional grids and grid data model. This method can accomplish the aggregation of any adjacent eight grids to form multiple nonrigid structures, making the original fixed relationship between parent and child grids more flexible. Second, the nonrigid grid structure optimizes the identification ability of grid vertices, grid edges, and grid faces. It has eight times the identification ability of the rigid grid structure. We exploited this structure for UAV identification with the same size grid at any position of the grid structure. Third, based on the nonrigid grid structure, an improved UAV conflict detection method and path planning method are designed. Experimental results indicate that, on an average, the efficiency of the improved conflict detection is an order of magnitude higher than that of the traditional computational detection method. The improved path planning method reduces the path length by an average of 11% compared to the single-grid planning method, and is 3.85 times more efficient than the multi-grid buffer planning method.

DQ-GAT: Towards Safe and Efficient Autonomous Driving With Deep Q-Learning and Graph Attention Networks

Autonomous driving in multi-agent dynamic traffic scenarios is challenging: the behaviors of road users are uncertain and are hard to model explicitly, and the ego-vehicle should apply complicated negotiation skills with them, such as yielding, merging and taking turns, to achieve both safe and efficient driving in various settings. Traditional planning methods are largely rule-based and scale poorly in these complex dynamic scenarios, often leading to reactive or even overly conservative behaviors. Therefore, they require tedious human efforts to maintain workability. Recently, deep learning-based methods have shown promising results with better generalization capability but less hand engineering efforts. However, they are either implemented with supervised imitation learning (IL), which suffers from dataset bias and distribution mismatch issues, or are trained with deep reinforcement learning (DRL) but focus on one specific traffic scenario. In this work, we propose DQ-GAT to achieve scalable and proactive autonomous driving, where graph attention-based networks are used to implicitly model interactions, and deep Q-learning is employed to train the network end-to-end in an unsupervised manner. Extensive experiments in a high-fidelity driving simulator show that our method achieves higher success rates than previous learning-based methods and a traditional rule-based method, and better trades off safety and efficiency in both seen and unseen scenarios. Moreover, qualitative results on a trajectory dataset indicate that our learned policy can be transferred to the real world for practical applications with real-time speeds. Demonstration videos are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://caipeide.github.io/dq-gat/</uri> .

Transportation Infrastructure as Catalyst: Evolving Urban Structure in the In-Between Space of Railway Stations

Struggling to cope with the pandemic, people are seeking for a more convenient and short-distance community lifestyle. The fourth industrial revolution has also given rise to more convenient transportation and information-based lifestyles. Around railway stations, the traditional circular planning method based on 5-10-15 min walking distance forms the centralized urban structure. When focus on the in-between space of multiple stations, sometimes there is still a vacuum of space that difficult to access by walking. Taking material transportation infrastructure (railway and connecting traffic modes such as buses and bicycles) as catalyst, this study explores the networked coordination of immaterial infrastructure (space, function, place, urban structure, etc.). The station area is redefined by different speeds of various connecting traffic modes, which improves the radiation and integration capacity of stations. Meanwhile, the multiplying of multiple isochrones is applied as another criteria for accessibility and land value, guiding the evolution of multi-centralized urban structure. It contributes to a more convenient community lifestyle as an urban design pandemic response. Furthermore, a definition method and quantitative analysis of 3D Station Realm is proposed relying on Urban Network Analysis (UNA), with practical exploration of Feixi Road Station urban design taken as an example based on the idea.

Negative Gradient Differential Biogeography-based Optimization for Mobile Robot Path Planning

Mobile robot path planning (MRPP) plays an irreplaceable role in the process of intelligent robots and practical artificial intelligence. The traditional global path planning methods have some shortcomings, such as difficulty in digging into environmental information and finding the optimal path effectively. To solve the above problems, this paper proposes a negative gradient differential biogeography-based optimization (NG-DBBO), which has strong local search ability and global optimization ability. Firstly, we present a differential migration approach to increase the population diversity in the iterative process of NG-DBBO, which can realize the information sharing between feature solutions effectively. Then a negative gradient descent strategy based on negative gradient descent is introduced to improve the learning rate, which not only enhances initial global search ability, but also avoids premature convergence. Noteworthily, the convergence of the algorithm is analyzed for single-peak and multi-peak problems respectively. After that, NG-DBBO is combined with the cubic spline interpolation to realize MRPP by the defined coding method and fitness function. The simulation experiments are used to demonstrate the availability of our method, which consist of two parts. In the first part, we select 23 benchmark functions to verify the accuracy and convergence speed of the NG-DBBO algorithm. The practicability of path planning in different environments is demonstrated in the second part.

Urban Planning and Design Layout Generation Based on Artificial Intelligence

Today’s cities are becoming more and more complex, the spatial layout is gradually becoming more and more complex, and all aspects of urban construction that need to be considered are increasing. The traditional urban planning and design methods have encountered new challenges. Based on the unique perspective of urban “mesoscale,” this study attempts to apply artificial intelligence technology in the early stage of urban planning and design, predict the positioning of design land based on the surrounding environment, so as to break the limitations of manual decision-making, explore the spatial layout problem from the perspective of machine, find the correlation between land data, and generate results with certain reference value to assist decision-making. By delimiting the research area and collecting and processing data, we trained and generated the artificial neural network model and selected three different areas for model test. The test results verify the feasibility and effectiveness of the method process.

Online trajectory planning for three-dimensional offshore boom cranes

Efficient control of offshore cranes has attracted world-wide attention in recent years. However, due to the persistent ship roll/heave motion disturbances, it is very challenging to guarantee the transient performance for offshore cranes. To solve this problem, this paper proposes an online trajectory planning method for three-dimensional offshore cranes. Specifically, preliminary trajectories are first designed for different crane motions (e.g., boom luffing rotating, rope hoisting motions), following which the cargoes can be delivered to designated sites. After that, some online adjustment terms are integrated with the pre-defined trajectories, which serve to attenuate or eliminate the undesired cargo swing introduced by both the improper crane operations and the ship motion disturbances. Different from traditional off-line planning methods, the proposed method retains satisfactory robustness (in the sense of swing elimination) due to the extra incorporated terms. The proposed control concept can be extended to trajectory planning of similar underactuated systems.

A hybrid inductive learning-based and deductive reasoning-based 3-D path planning method in complex environments

Traditional path planning methods, such as sampling-based and iterative approaches, allow for optimal path’s computation in complex environments. Nonetheless, environment exploration is subject to rules which can be obtained by domain experts and could be used for improving the search. The present work aims at integrating inductive techniques that generate path candidates with deductive techniques that choose the preferred ones. In particular, an inductive learning model is trained with expert demonstrations and with rules translated into a reward function, while logic programming is used to choose the starting point according to some domain expert’s suggestions. We discuss, as use case, 3-D path planning for neurosurgical steerable needles. Results show that the proposed method computes optimal paths in terms of obstacle clearance and kinematic constraints compliance, and is able to outperform state-of-the-art approaches in terms of safety distance-from-obstacles respect, smoothness, and computational time.

A Global Path Planning Method for Unmanned Ground Vehicles in Off-Road Environments Based on Mobility Prediction

In a complex off-road environment, due to the low bearing capacity of the soil and the uneven features of the terrain, generating a safe and effective global route for unmanned ground vehicles (UGVs) is critical for the success of their motion and mission. Most traditional global path planning methods simply take the shortest path length as the optimization objective, which makes it difficult to plan a feasible and safe route in complex off-road environments. To address this problem, this research proposes a global path planning method, which considers the influence of terrain factors and soil mechanics on UGV mobility. First, we established a high-resolution 3D terrain model with remote sensing elevation terrain data, land use and soil type distribution data, based on a geostatistical method. Second, we analyzed the vehicle mobility by the terramechanical method (i.e., vehicle cone index and Bakker’s theory), and then calculated the mobility cost based on a fuzzy inference method. Finally, based on the calculated mobility cost, the probabilistic roadmap method was used to establish the connected matrix and the multi-dimensional traffic cost evaluation matrix among the sampling nodes, and then an improved A* algorithm was proposed to generate the global route.

Multi-Objective Optimal Sizing of HRES under Multiple Scenarios with Undetermined Probability

In recent years, technologies for renewable energy utilization have been booming. Hybrid renewable energy systems (HRESs), integrating multiple energy sources to mitigate the unstable, unpredictable, and intermittent characteristics of a single renewable energy source, have become increasingly popular. However, due to the inherent intermittency and uncertainty of renewable energies, the impact of uncertain factors on the capacity optimization of HRESs needs to be considered. In the traditional scenario-based planning method, when dealing with uncertain factors, the probability corresponding to the scenario is difficult to determine. Furthermore, when applying the robust optimization method, it is difficult to fully use existing data to describe uncertain parameters in the form of intervals. To tackle these difficulties, this study proposes a probability undetermined scenario-based sizing model (PUSS model) for stand-alone HRES configuration optimization and a multi-objective evolutionary algorithm as the problem solver. The solution set obtained by the method covers multiple possible values of scenario probability combinations and can provide decision-makers with an overview of alternatives for HRES sizing under different power supply pressures. Based on the real environment data and load data of a certain place, the proposed model and algorithm are applied to sizing a typical HRES comprising wind generators, solar photovoltaic panels, energy-storage devices, and diesel generators. The experimental results show that the proposed PUSS method is both effective and efficient.

Domain Concretization From Examples: Addressing Missing Domain Knowledge Via Robust Planning

The assumption of complete domain knowledge is unwarranted for robot planning and decision-making in the real world. Incompleteness in domain knowledge may come from design flaws or arise from domain ramifications or qualifications. In such cases, traditional planning methods can produce highly undesirable behaviors. Addressing the planning problem under incomplete domain knowledge is challenging since the agent has no clue about what information is missing. This is a type of unknown unknowns, which differs significantly from partial observability, a type of known unknowns. In this work, we assume that the missing information is encoded in a set of examples or teacher demonstrations. We formulate the problem of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">domain concretization</i> with these examples as an inverse problem to domain abstraction. Given a domain model provided initially, when the model does not conform with the teacher demonstrations, our method searches for a candidate model set that refines the initial model under a minimalistic and deterministic model assumption. For new problems, it generates a robust plan with the maximum probability of success under the set of candidate models. Together with a standard search formulation in the model-space, we propose a heuristic-based search method and also an online version of it to reduce the search time. We evaluated our approach with several International Planning Competition (IPC) domains and a simulated robotics domain where incompleteness was introduced by removing domain features from the complete models. Results show that our methods increase the success rate of planning without significantly impacting the plan cost.

Optimal DC Microgrid Operation with Model Predictive Control-Based Voltage-Dependent Demand Response and Optimal Battery Dispatch

Recently, the integration of optimal battery dispatch and demand response has received much attention in improving DC microgrid operation under uncertainties in the grid-connect condition and distributed generations. However, the majority of prior studies on demand response considered the characteristics of global frequency variable instead of the local voltage for adjusting loads, which has led to obstacles in operating DC microgrids in the context of increasingly rising power electronic loads. Moreover, the consideration of voltage-dependent demand response and optimal battery dispatch has posed challenges for the traditional planning methods, such as stochastic programming, because of nonlinear constraints. Considering these facts, this paper proposes a model predictive control-based integrated voltage-based demand response and batteries’ optimal dispatch operation for minimizing the entire DC microgrid’s operating cost. In the proposed model predictive control approach, the binary decisions about voltage-dependent demand response and charging or discharging status of storage batteries are determined using a deep-Q network-based reinforcement learning method to handle uncertainties in various operating conditions (e.g., AC grid-connect faults and DC sources variations). It also helps to improve the DC microgrid operation efficiency in the two aspects: continuously avoiding load shedding or shifting and reducing the batteries’ charge and discharge cycles to prolong their service life. Finally, the proposed method is validated by comparing to the stochastic programming-based model predictive control method. Simulation results show that the proposed method obtains convergence with approximately 41.95% smaller operating cost than the stochastic optimization-based model predictive control method.

Analysis of Ice and Snow Path Planning System Based on MNN Algorithm.

Traditional ice and snow path planning methods still have internal environmental problems in intelligent path planning, such as weak innovation ability, imperfect management, long planning path, unreasonable security structure, and low degree of specialization. Therefore, more and more ice and snow sports lovers are eager to solve this problem. This paper designs a path planning method based on three-dimensional ice and snow model. The path planning method of moving snow and ice based on MNN (Multiclass Neural Networks) algorithm is studied from many aspects. MNN algorithm is used for comprehensive analysis and evaluation. The mobile phone provides data information on key nodes, air resistance, momentum change, ice and snow movement track, and so on. The results show that the ice and snow path planning system based on MNN algorithm designed in this paper has the advantages of high feasibility, high data accuracy, and good prediction effect and can effectively improve the efficiency of ice and snow path planning.

Community Public Open Space Planning Based on Green Infrastructure with the Priority of Biodiversity

Maintaining and restoring urban biodiversity is one of the basic functions of green infrastructure. Starting from the ecological problems of community public open space and the insufficiency of traditional planning methods, the biodiversity of green infrastructure attempts to be integrated into public open space planning in community-scale. After discussing the research on the combination of community public open space and green infrastructure based on biodiversity, ‘the interconnections between green infrastructures’ is the key to achieving both benefits at the same time. Finally, taking Nanjing Xinjiekou Street in China as an example, the renewal planning method of community public open space on the priority of biodiversity is elaborated and corresponding suggestions are put forward. In a word, the reasonable planning and layout of community green infrastructure based on biodiversity can make up for the disadvantages of traditional planning and renewal methods of public open space to a certain extent, solve the ecological and social problems of public open space, and realize the symbiosis of people and nature in community.

Scenario planning: A dynamic and collaborative tool for uncertain conditions

Uncertainty demands a nimble approach to preparing for future conditions that are nearly impossible to forecast. Traditional airport planning vehicles such as master plans, strategic and business plans have long been the tools utilised by industry leaders to determine financial needs, identification of the physical infrastructure necessary to accommodate demand as well as the human capital and tactics required for a high-performance organisation. Traditional planning methods remain useful, valuable tools and serve a specific purpose to organise the efforts of an airport organisation in preparation for future conditions that can be reasonably predicted in a singular forecast or a range of forecasts. Scenario planning, in contrast, imagines a much broader range of futures than the typical forecast models are able to consider in traditional planning exercises. This tool has been used by numerous organisations including Royal Dutch Shell, for collaborative efforts among diverse teams to imagine numerous contrasting futures. The journey down the scenario planning pathway takes into consideration a broader range of influencing factors than most traditional planning efforts forcing a team to expand their aperture beyond the day-to-day information that drives decision making. Memories of the contrasting futures that are devised in this process rarely come to fruition in their entirety with fragments of each of the imagined futures more likely assembling to form the reality experienced by the team. Organisations are better equipped to react to events as they unfold with these memories formulated and top of mind, particularly during uncertain times. Many have come to find scenario planning as a powerful tool to drive creative thought processes and team dialogue with the results contributing to better-informed traditional planning exercises.

A Novel Prediction and Planning Model for the Benefit of Irrigation Water Allocation Based on Deep Learning and Uncertain Programming

Due to population growth and human activities, water shortages have become an increasingly serious concern in the North China Plain, which has become the world’s largest underground water funnel. Because the yield per unit area, planting area of crops, and effective precipitation in the region are uncertain, it is not easy to plan the amount of irrigation water for crops. In order to improve the applicability of the uncertainty programming model, a hybrid LSTM-CPP-FPP-IPP model (long short-term memory, chance-constrained programming, fuzzy possibility programming, interval parameter programming) was developed to plan the irrigation water allocation of irrigation system under uncertainty. The LSTM (long short-term memory) model was used to predict crop yield per unit area, and CPP-FPP-IPP programming (chance-constrained programming, fuzzy possibility programming, interval parameter programming) was used to plan the crop area and the effective precipitation under uncertainty. The hybrid model was used for the crop production profit of winter wheat and summer corn in five cities in the North China Plain. The average absolute error between the model prediction value and the actual value of the yield per unit area of winter wheat and summer maize in four cities in 2020 was controlled within the range of 14.02 to 696.66 kg/hectare. It shows that the model can more accurately predict the yield per unit area of crops. The planning model for the benefit of irrigation water allocation generated three scenarios of rainfall level and four planting intentions, and compared the planned scenarios with the actual production benefits of the two crops in 2020. In a dry year, the possibility of planting areas for winter wheat and summer corn is optimized. Compared with the traditional deterministic planning method, the model takes into account the uncertain parameters, which helps decision makers seek better solutions under uncertain conditions.

Research on image stitching method based on improved ORB and stitching line calculation

Image matching is one of the hot issues in recent years. Image matching determines the effect of image mosaic to a large extent. Therefore, a stable, accurate, and fast image matching algorithm is very important for the subsequent processing of image information. Most of the traditional image matching methods have the problems of high-error matching rate and difficulty in to eliminating false matching. To resolve the shortcomings listed here, an improved algorithm was proposed in which a combined measure of Hamming distance. Similarity measurement with Tanimoto similarity measurement was adopted to dispose binary feature vectors. In addition, as an important step in the process of image mosaics, the calculation of the best mosaics can eliminate the ghosting and ghosting that may appear in the mosaics. However, the traditional dynamic planning method is easy to pass through the obstacles and form obvious stitching traces when searching for stitching lines in images with large obstacles. In view of the aforementioned content, we propose a fast and robust method to search the image difference matrix and structure difference matrix of the overlapping area of the input image according to the search strategy to determine the pixel coordinates of the splicing line. Finally, from an optimal splicing line, the experimental results show that our algorithm performs well for eliminating error matching points. Compared with traditional algorithms, matching accuracy is improved by 25%. In terms of calculating the optimal splicing line, the method proposed is faster than the traditional method and has good robustness.

A systematic framework for urban smart transportation towards traffic management and parking

&lt;abstract&gt; &lt;p&gt;Considering the wide applications of big data in transportation, machine learning and mobile internet technology, artificial intelligence (AI) has largely empowered transportation systems. Many traditional transportation planning and management methods have been improved or replaced with smart transportation systems. Hence, considering the challenges posed by the rising demand for parking spaces, traffic flow and real-time operational management in urban areas, adopting artificial intelligence technologies is crucial. This study aimed to establish a systematic framework for representative transportation scenarios and design practical application schemes. This study begins by reviewing the development history of smart parking systems, roads and transportation management systems. Then, examples of their typical application scenarios are presented. Second, we identified several traffic problems and proposed solutions in terms of a single parking station, routes and traffic networks for an entire area based on a case study of a smart transportation systematic framework in the Xizhang District of Wuxi City. Then, we proposed a smart transportation system based on smart parking, roads and transportation management in urban areas. Finally, by analyzing these application scenarios, we analyzed and predicted the development directions of smart transportation in the fields of smart parking, roads and transportation management systems.&lt;/p&gt; &lt;/abstract&gt;

Real vs. Virtual City: Planning Issues in a Discontinuous Urban Area in Budapest’s Inner City

&lt;p&gt;The 21st century has brought fundamental changes in the development of cities, with the spread of ICT and the rise of digitalization. The new technologies are increasingly making their mark on urban planning and policy as well. The question of how contemporary urban planning is adapting to new challenges is particularly relevant as neighborhoods built in previous centuries and decades by traditional planning methods are now increasingly confronted with new public and environmental demands. Despite the bad reputation of Budapest’s 8th district, Józsefváros, based on the socio-economic and urban problems it has continuously faced in the past, the neighborhood has become one of the most dynamically developing urban areas in the last decade. From a planning point of view, an exciting area of the district is Szigony Street and its wider surroundings due to the strongly fragmented, heterogeneous urban fabric. Nevertheless, the only high-rise mass housing estate built in Budapest’s historic inner city in the 1960s and 1970s is located there. Our research used a complex methodology (document, content and database analysis, fieldwork, surveys with professionals, and interviews) to explore the planning history of the area’s development. Ultimately, the aim was to identify the most important outcomes and consequences of traditional and contemporary planning and design and whether modern digital planning can make a meaningful contribution to the development of the neighborhood. Our results show that urban planning and development in Budapest are still essentially based on traditional top-down approaches. Digitalization has a role to play primarily in visualization and contextualization but digitalizing of planning alone will not solve problems and past planning mistakes that affect the urban fabric of a neighborhood.&lt;/p&gt;