Research on Anthropomorphic Obstacle Avoidance Trajectory Planning for Adaptive Driving Scenarios Based on Inverse Reinforcement Learning Theory

The forward design of trajectory planning strategies requires preset trajectory optimization functions,resulting in poor adaptability of the strategy and an inability to accurately generate obstacle avoidance trajectories that conform to real driver behavior habits.In addition,owing to the strong time-varying dynamic characteristics of obstacle avoidance scenarios,it is necessary to design numerous trajectory optimization functions and adjust the corresponding parameters.Therefore,an anthropomorphic obstacle-avoidance trajectory planning strategy for adaptive driving scenarios is proposed.First,numerous expert-demonstrated trajectories are extracted from the HighD natural driving dataset.Subsequently,a trajectory expectation feature-matching algorithm is proposed that uses maximum entropy inverse reinforcement learning theory to learn the extracted expert-demonstrated trajectories and achieve automatic acquisition of the optimization function of the expert-demonstrated trajectory.Furthermore,a mapping model is constructed by combining the key driving scenario information that affects vehicle obstacle avoidance with the weight of the optimization function,and an anthropomorphic obstacle avoidance trajectory planning strategy for adaptive driving scenarios is proposed.Finally,the proposed strategy is verified based on real driving scenarios.The results show that the strategy can adjust the weight distribution of the trajectory optimization function in real time according to the“emergency degree”of obstacle avoidance and the state of the vehicle.Moreover,this strategy can generate anthropomorphic trajectories that are similar to expert-demonstrated trajectories,effectively improving the adaptability and acceptability of trajectories in driving scenarios.

Adaptive Space Expansion for Fast Motion Planning

The sampling process is very inefficient for sam-pling-based motion planning algorithms that excess random sam-ples are generated in the planning space.In this paper,we pro-pose an adaptive space expansion(ASE)approach which belongs to the informed sampling category to improve the sampling effi-ciency for quickly finding a feasible path.The ASE method enlarges the search space gradually and restrains the sampling process in a sequence of small hyper-ellipsoid ring subsets to avoid exploring the unnecessary space.Specifically,for a con-structed small hyper-ellipsoid ring subset,if the algorithm cannot find a feasible path in it,then the subset is expanded.Thus,the ASE method successively does space exploring and space expan-sion until the final path has been found.Besides,we present a particular construction method of the hyper-ellipsoid ring that uniform random samples can be directly generated in it.At last,we present a feasible motion planner BiASE and an asymptoti-cally optimal motion planner BiASE*using the bidirectional exploring method and the ASE strategy.Simulations demon-strate that the computation speed is much faster than that of the state-of-the-art algorithms.The source codes are available at https://github.com/shshlei/ompl.

Path Planning for Robotic Arms Based on an Improved RRT Algorithm

The burgeoning robotics industry has catalyzed significant strides in the development and deployment of industrial and service robotic arms, positioning path planning as a pivotal facet for augmenting their operational safety and efficiency. Existing path planning algorithms, while capable of delineating feasible trajectories, often fall short of achieving optimality, particularly concerning path length, search duration, and success likelihood. This study introduces an enhanced Rapidly-Exploring Random Tree (RRT) algorithm, meticulously designed to rectify the issues of node redundancy and the compromised path quality endemic to conventional RRT approaches. Through the integration of an adaptive pruning mechanism and a dynamic elliptical search strategy within the Informed RRT* framework, our algorithm efficiently refines the search tree by discarding branches that surpass the cost of the optimal path, thereby refining the search space and significantly boosting efficiency. Extensive comparative analysis across both two-dimensional and three-dimensional simulation settings underscores the algorithm’s proficiency in markedly improving path precision and search velocity, signifying a breakthrough in the domain of robotic arm path planning.

Joint mission and route planning of unmanned air vehicles via a learning-based heuristic

Unmanned air vehicles(UAVs) have been regularly employed in modern wars to conduct different missions. Instead of addressing mission planning and route planning separately,this study investigates the issue of joint mission and route planning for a fleet of UAVs. The mission planning determines the configuration of weapons in UAVs and the weapons to attack targets, while the route planning determines the UAV’s visiting sequence for the targets. The problem is formulated as an integer linear programming model. Due to the inefficiency of CPLEX on large scale optimization problems, an effective learningbased heuristic, namely, population based adaptive large neighborhood search(P-ALNS), is proposed to solve the model. In P-ALNS, seven neighborhood structures are designed and adaptively utilized in terms of their historical performance. The effectiveness and superiority of the proposed model and algorithm are demonstrated on test instances of small, medium and large sizes. In particular, P-ALNS achieves comparable solutions or as good as those of CPLEX on small-size(20 targets)instances in much shorter time.

Indirect Carbon Reduction Path from the Perspective of Urban Climate Adaptation Planning

Since the 21^(st)century,the problem of global warming has been prominent,and the problem of climate change has attracted worldwide attention.All countries have issued urban climate adaptation planning policy documents to address the current climate problem.At the same time,the proposal of the“double carbon”vision allows us to focus on the carbon emission reduction of cities and buildings.In addition to the implementation of the direct carbon reduction approach in the whole life cycle of construction,the functional role of indirect carbon reduction cannot be underestimated.By analyzing the domestic and foreign urban climate adaptation planning policy documents,summarizing the indirect carbon reduction approaches,and analyzing the feasibility of the indirect carbon reduction and emission reduction methods from the perspective of urban climate adaptation planning,the indirect carbon reduction adaptation strategy is proposed,which provides a reference for the implementation of urban climate adaptation planning and the target completion of reaching the carbon neutralization and peak on time.

Complete Coverage Path Planning Based on Improved Area Division

It is difficult to solve complete coverage path planning directly in the obstructed area. Therefore, in this paper, we propose a method of complete coverage path planning with improved area division. Firstly, the boustrophedon cell decomposition method is used to partition the map into sub-regions. The complete coverage paths within each sub-region are obtained by the Boustrophedon back-and-forth motions, and the order of traversal of the sub-regions is then described as a generalised traveling salesman problem with pickup and delivery based on the relative positions of the vertices of each sub-region. An adaptive large neighbourhood algorithm is proposed to quickly obtain solution results in traversal order. The effectiveness of the improved algorithm on traversal cost reduction is verified in this paper through multiple sets of experiments. .

Energy hub-based optimal planning for integrated energy systems considering part-load characteristics and synergistic effect of equipment

Integrated energy systems(lESs)represent a promising energy supply model within the energy internet.However,multi-energy flow coupling in the optimal configuration of IES results in a series of simplifications in the preliminary planning,affecting the cost,efficiency,and environmental performance of IES.A novel optimal planning method that considers the part-load characteristics and spatio-temporal synergistic effects of IES components is proposed to enable a rational design of the structure and size of IES.An extended energy hub model is introduced based on the“node of energy hub”concept by decomposing the IES into different types of energy equipment.Subsequently,a planning method is applied as a two-level optimization framework-the upper level is used to identify the type and size of the component,while the bottom level is used to optimize the operation strategy based on a typical day analysis method.The planning problem is solved using a two-stage evolutionary algorithm,combing the multiple-mutations adaptive genetic algorithm with an interior point optimization solver,to minimize the lifetime cost of the IES.Finally,the feasibility of the proposed planning method is demonstrated using a case study.The life cycle costs of the IES with and without consideration of the part-load characteristics of the components were$4.26 million and$4.15 million,respectively,in the case study.Moreover,ignoring the variation in component characteristics in the design stage resulted in an additional 11.57%expenditure due to an energy efficiency reduction under the off-design conditions.

Green Space System Planning in Shuangcheng City and Creation of the City's Characteristics

Characteristic urban construction has already become a trend of modern social development,in this context,components of characteristic urban green spaces,and ways of demonstrating features of urban green space system were analyzed.Shuangcheng City in Heilongjiang Province was taken for example to analyze planning objectives and principles of characteristic urban green space system.Moreover,the green space layout of "A Hundred Years Old Castle,A Hundred of Enterprises and A Hundred of Gardens" was introduced.It was proposed that green space system planning suited to the development features and ecological conditions of the city should be developed from the perspective of greater regional environment,historical and cultural deposits,urban development pattern and lifestyle of the locals,so as to create urban green space system with regional features.

Water environmental planning considering the influence of non-linear characteristics

In practical water environmental planning, the influence of the non-linear characteristics on the benefit of environmental investment was seldom taken into consideration. This paper demonstrates that there exist a lot of non-linear behaviors in water environment by emphatically analyzing the influence of the non-linear characteristics of the economic scale, the meandering river and the model on water environmental planning, which will make a certain impact on the water environmental planning that sometimes cannot be neglected. This paper also preliminarily explores how to integrate the non-linear characteristics into water environmental planning. The results showed that compared with traditional methods, water environmental planning considering non-linear characteristics has its prevalence and it is necessary to develop the relevant planning theories and methods.

CAD Model-Based Intelligent Inspection Planning for Coordinate Measuring Machines

As the market competition among enterprises grows intensively and the demand for high quality products increases rapidly, product quality inspection and control has become one of the most important issues of manufacturing, and improving the efficiency and accuracy of inspection is also one of problems which enterprises must solve. It is particularly important to establish rational inspection planning for parts before inspecting product quality correctly. The traditional inspection methods have been difficult to satisfy the requirements on the speed and accuracy of modern manufacturing, so CAD-based computer-aided inspection planning （CAIP） system with the coordinate measuring machines （CMM） came into being. In this paper, an algorithm for adaptive sampling and collision-free inspection path generation is proposed, aiming at the CAD model-based inspection planning for coordinate measuring machines （CMM）. Firstly, using the method of step adaptive subdivision and iteration , the sampling points for the specified number with even distribution will be generated automatically. Then, it generates the initial path by planning the inspection sequence of measurement points according to the values of each point＇s weight sum of parameters, and detects collision by constructing section lines between the probe swept-volume surfaces and the part surfaces, with axis-aligned bounding box （AABB） filtering to improve the detection efficiency. For collided path segments, it implements collision avoidance firstly aiming at the possible outer-circle features, and then at other collisions, for which the obstacle-avoiding movements are planned with the heuristic rules, and combined with a designed expanded AABB to set the obstacle-avoiding points. The computer experimental results show that the presented algorithm can plan sampling points＇ locations with strong adaptability for different complexity of general surfaces, and generate efficient optimum path in a short time and avoid collision effectively.

Solving Multitrip Pickup and Delivery Problem With Time Windows and Manpower Planning Using Multiobjective Algorithms

The multitrip pickup and delivery problem with time windows and manpower planning(MTPDPTW-MP)determines a set of ambulance routes and finds staff assignment for a hospital. It involves different stakeholders with diverse interests and objectives. This study firstly introduces a multiobjective MTPDPTW-MP(MO-MTPDPTWMP) with three objectives to better describe the real-world scenario. A multiobjective iterated local search algorithm with adaptive neighborhood selection(MOILS-ANS) is proposed to solve the problem. MOILS-ANS can generate a diverse set of alternative solutions for decision makers to meet their requirements. To better explore the search space, problem-specific neighborhood structures and an adaptive neighborhood selection strategy are carefully designed in MOILS-ANS. Experimental results show that the proposed MOILS-ANS significantly outperforms the other two multiobjective algorithms. Besides, the nature of objective functions and the properties of the problem are analyzed. Finally, the proposed MOILS-ANS is compared with the previous single-objective algorithm and the benefits of multiobjective optimization are discussed.

Self-adapting control parameters modifieddifferential evolution for trajectoryplanning of manipulators

Control parameters of original differential evolution （DE） are kept fixed throughout the entire evolutionary process. However, it is not an easy task to properly set control parameters in DE for different optiinization problems. According to the relative position of two different individual vectors selected to generate a difference vector in the searching place, a self-adapting strategy for the scale factor F of the difference vector is proposed. In terms of the convergence status of the target vector in the current population, a self-adapting crossover probability constant CR strategy is proposed. Therefore, good target vectors have a lower CFI while worse target vectors have a large CFI. At the same time, the mutation operator is modified to improve the convergence speed. The performance of these proposed approaches are studied with the use of some benchmark problems and applied to the trajectory planning of a three-joint redundant manipulator. Finally, the experiment results show that the proposed approaches can greatly improve robustness and convergence speed.

Capacity Building for the Integration of Climate Adaptation into Urban Planning Processes: The Dutch Experience

The institutions of the Dutch (urban) planning system face four challenging characteristics of climate adaptation measures. These measures are uncertain in their effects, in competition with other interests, multifaceted, and inherently complex. Capacity building is a key issue for the implementation of climate adaptation measures in urban planning processes, which aim to achieve Climate-Proof Cities (CPC). For successful capacity building, it is important to define the relevant stakeholders and tailor the adaptation strategies first to (the position of) these stakeholders and next to the specific urban conditions and issues. In addition, scientific insights and tools can be of assistance, and the use of climate maps can help to create a common language. Such common understanding of climate problems can lead to “goal entwinement” between actors, which can support the implementation of climate adaptation strategies in urban planning. Awareness, recognition and urgency are the most important components of this common understanding, which may differ for each stage in every urban planning process. In order to overcome the pragmatism that rules in day-to-day urban planning processes, multi-level arrangements between different tiers of government must be employed to improve the penetration of climate adaptation measures. After all, it still remains a soft interest in a hard process.

Planning and Design of Dashuigou Reserve Station in Fengtongzhai Nature Reserve of Sichuan Province

Characteristics of Fengtongzhai Nature Reserve were analyzed from the perspective of animal and plant resources and cultural type. Design concepts of Dashuigou Reserve Station were proposed by combining with site characteristics, and key design points were summarized.

Augmented planning support system framework for mountainous urban master planning

As a reflection of the relationship between human and mountainous environment, urban planning has an impact on the mountainous environment by changing the topography, landform and spatial layout. A good urban planning can mitigate and adapt to the mountainous environmental impact. Urban master planning involves the interrelationships and interactions of various components of urban complex systems. Planning Support System(PSS), as a technical means to assist planning decision-making, is mostly based on the construction mode of "user(stakeholder)-system". Its strong professional characteristics are not conducive to the consensus of diverse stakeholders on urban planning. The aim of this paper is therefore to build an augmented planning support system framework that is based on complex adaptive system theory, this framework is ontology-driven, and thus will enable the generation of a planning support prototype system for mountainous urban master planning founded on this framework. The framework fuses the urban planning ontology and the planning support system together, which helps different urban agents to reach a consensus based on a common understanding of urban planning. The defect is that the construction of the urban planning ontology is still manually constructed. The approach advocated here will enable a common understanding of mountainous urban master planning, support efficient and flexible decision in this area, and provide reference framework for future mountainous urban master PSS developments and application. The PSS prototype developed based on augmented planning support system framework has been applied to the urban master planning of Changting County in Fujian Province, China. Through the application of multiscenario analysis, urban agents can deepen their understanding of the current situation and future development of the city, and ultimately helps to promote urban planning decisions and implementation.

The Development History and Prospect Forecast of Characteristic Towns from the Perspective of Planning Rationality

Most of the studies on characteristic towns have made policy interpretation and planning convergence as the research focus. There is still a lack of in-depth discussion on the connotation, essence and scientificalness of characteristic towns. Starting from planning rationality and the source of characteristic towns, this study summarizes the cultivation process and essence of characteristic towns, looks forward to its development prospects and expounds the connotation, innovation and scientificalness of characteristic towns in east China’s Zhejiang Province and other places. The study believes that the innovation of the characteristic town Zhejiang model is reflected in the three aspects of industrial choice, space carrier and system design, but there are rational logical faults from the existing cases to the promotion of the whole province, from Zhejiang experience to national promotion. From the perspective of planning rationality, the construction of characteristic towns in China is a development model under the leadership of political rationality. Planners should clearly understand and provide technical rational support to improve the practice of characteristic towns.

Investigating climate change vulnerability and planning for adaptation: Learning from a study of climate change impacts on the Mountain Gorilla in the Albertine Rift

Climate change represents an unprecedented challenge for the conservation and management of endangered species and habitats. Effective climate smart conservation will require robust predictions of vulnerability and future changes, along with the design and prioritisation of effective adaptation planning and management responses that are clearly linked to projected climate impacts. To achieve this goal, conservation managers urgently need practical tools and approaches for vulnerability assessment and adaptation planning. This article explores lessons emerging from a recent vulnerability assessment and adaptation planning exercise conducted on the impact of climate change for mountain gorilla (Gorilla beringei beringei). We describe the main findings emerging from this initiative and explore key lessons for climate change vulnerability assessment and adaptation planning for conservation management. Data limitations were a key factor determining the utility of model outputs and we stress the importance of stakeholder engagement and collaboration throughout the vulnerability assessment and adaptation planning cycle. These findings are of relevance to conservation practitioners seeking to incurporate climate change considerations into ongoing management planning for endangered species conservation.

Urban Heat Island Mitigation and Urban Planning: The Case of the Mexicali, B. C. Mexico

The derivations of climate change on cities and their activities depend on their capacity for adaptation and mitigation. In this sense, it has long recognized the influence of cities on their own climate, which is typically warmer than the surrounding. This phenomenon called urban heat island (UHI) has a number of impacts on air quality, water demand and energy. Since the fourth IPCC assessment report indicates the need for urban centers devoting efforts to adaptation to reduce the risks of direct and indirect impacts of climate change. The same organization recognizes the urban planning as a tool to seek such order. However, it also recognizes that the current scale of climate models cannot provide a representation of urban areas. This paper explores the intensification of the UHI, its relationship with urban expansion and its impact on housing in the city of Mexicali, B.C. Its aim is to determine its impact and mitigation potential through analysis and modeling of urban structure, expressed in use and land cover, as well as the implementation of mitigation strategies. The results show on the one hand, the convenience of using dynamic modeling as a tool applied to urban planning with a focus on mitigation and adaptation to climate change. Furthermore, regarding the implementation of strategies, the results show that most efficiency is obtained when applied generally, this is, considering green and cool roofs, cool pavements and afforestation as part of urbanization process, otherwise, only partial results are achieved. Overall, housing land use has significant potential to mitigate the UHI in the city.

Time-optimal trajectory planning based on improved adaptive genetic algorithm

This paper investiga tes a trajectory planning algorithm to reduce the manipulator’s working time.A t ime-optimal trajectory planning(TOTP)is conducted based on improved ad aptive genetic algorithm(IAGA)and combined with cubic triangular Bezier spline(CTBS).The CTBS based trajectory planning we did before can achieve continuous second and third derivation,hence it meets the stability requirements of the m anipulator.The working time can be greatly reduced by applying IAGA to the puma 560 trajectory planning when considering physical constraints such as angular ve locity,angular acceleration and jerk.Simulation experiments in both Matlab and ADAMS illustrate that TOTP based on IAGA can give a time optimal result with sm oothness and stability.

Planning strategy of gully areas in mountainous cities adapting to ecological process

With the expansion of mountainous cities,many gully areas have been included in the scope of urban construction land.These gully areas will restrict urban construction because of sensitivity and instability caused by the unique ecological processes.This paper attempts to explore a planning and design strategy that combines ecological processes to reduce the risk of urban construction in gully areas.According to the ecological characteristics of gully areas,especially its suitability for urban construction,gully areas can be divided into three categories in the mountainous city:prevention and controlling gully—focusing on gully drainage;conservation gully—focusing on the construction of green space;utilization type gully—focusing on the development of gully towns.The utilization type gully can be used as urban construction land.For utilization type gullies,horizontal and vertical segmented planning strategies can adopt their respective ecological processes by land utilization,landscape construction,spatial layout and traffic organization matching the time-space natural process to form harmony human settlements.