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The purpose of this paper is to present a new nested rapidly‐exploring random tree (RRT) algorithm for fault tolerant motion planning of robotic manipulators.

Another RRT algorithm is nested within the general RRT algorithm. This second nested level is used to check whether the new sampled node in the first nested level is fault tolerant. If a solution can be found in the second nested RRT, the reduced manipulator after failures at the new sampled node can still fulfill the remaining task and this new sampled node is added into the nodes of RRT in the first level. Thus, the nodes in the first level RRT algorithm are all fault tolerant postures. The final trajectory joined by these nodes is also obviously fault tolerant. Besides fault tolerance, this new nested RRT algorithm also can fulfill some secondary tasks such as improvement of dexterity and obstacle avoidance. Sufficient simulations and experiments of this new algorithm on fault tolerant motion planning of robotic manipulators are implemented.

It is found that the new nested RRT algorithm can fulfill fault tolerance and some other secondary tasks at the same time. Compared to other existing fault tolerant algorithms, this new algorithm is more efficient.

The paper presents a new nested RRT algorithm for fault tolerant motion planning.

The goal of this research is to develop a dynamic step path planning algorithm based on the rapidly exploring random tree (RRT) algorithm that combines Q-learning with the Gaussian distribution of obstacles. A route for autonomous vehicles may be swiftly created using this algorithm.

The path planning issue is divided into three key steps by the authors. First, the tree expansion is sped up by the dynamic step size using a combination of Q-learning and the Gaussian distribution of obstacles. The invalid nodes are then removed from the initially created pathways using bidirectional pruning. B-splines are then employed to smooth the predicted pathways.

The algorithm is validated using simulations on straight and curved highways, respectively. The results show that the approach can provide a smooth, safe route that complies with vehicle motion laws.

An improved RRT algorithm based on Q-learning and obstacle Gaussian distribution (QGD-RRT) is proposed for the path planning of self-driving vehicles. Unlike previous methods, the authors use Q-learning to steer the tree's development direction. After that, the step size is dynamically altered following the density of the obstacle distribution to produce the initial path rapidly and cut down on planning time even further. In the aim to provide a smooth and secure path that complies with the vehicle kinematic and dynamical restrictions, the path is lastly optimized using an enhanced bidirectional pruning technique.

The purpose of this paper is to improve the performance of the genetic algorithm-based compliant robot path planning (GACRPP) in complex dynamic environment by proposing an improved bidirectional rapidly exploring random tree (Bi-RRT)-based population initialization method.

To achieve GACRPP in complex dynamic environment with high performance, an improved Bi-RRT-based population initialization method is proposed. First, the grid model is adopted to preprocess the working space of mobile robot. Second, an improved Bi-RRT is proposed to create multi-cluster connections between the starting point and the goal point. Third, the backtracking method is used to generate the initial population based on the multi-cluster connections generated by the improved Bi-RRT. Subsequently, some comparative experiments are implemented where the performances of the improved Bi-RRT-based population initialization method are compared with other population initialization methods, and the comparison results of the improved genetic algorithm (IGA) combining with the different population initialization methods are shown. Finally, the optimal path is further smoothed with the help of the technique of quadratic B-spline curves.

It is shown in the experiment results that the improved Bi-RRT-based population initialization method is capable of deriving a more diversified initial population with less execution time and the IGA combining with the proposed population initialization method outperforms the one with other population initialization methods in terms of the length of optimal path and the execution time.

In this paper, the Bi-RRT is introduced as a population initialization method into the GACRPP problem. An improved Bi-RRT is proposed for the purpose of increasing the diversity of initial population. To characterize the diversity of initial population, a new notion of breadth is defined in terms of Hausdorff distance between different paths.

The purpose of this paper is to characterise the TWIN-RRT* algorithm which solves a motion planning problem in which an agent has multiple possible targets where none of them is compulsory and retrieves feasible, “low cost”, asymptotically optimal and probabilistically complete paths. The TWIN-RRT* algorithm solves path planning problems for both holonomic and non-holonomic robots with or without kinematic constraints in a 2D environment.

It was designed to work equally well with higher degree of freedom agents in different applications. It provides a practical implementation of feasible and fast planning, namely where a closed loop is required. Initial and final configurations are allowed to be exactly the same.

The TWIN-RRT* algorithm computes an efficient path for a single agent towards multiple targets where none of them is mandatory. It inherits the low computational cost, probabilistic completeness and asymptotical optimality from RRT*.

It uses efficiency as cost function, which can be adjusted to the requirements of any given application. TWIN-RRT also shows compliance with kinematic constraints.

The practical application where this work has been used consists of an autonomous mobile robot that picks up golf balls in a driving range. The multiple targets are the golf balls and the optimum path is a requirement to reduce the time and energy to refill as quickly as possible the balls dispensing machine.

The new random sampling algorithm – TWIN-RRT* – is able to generate feasible efficient paths towards multiple targets retrieving closed-loop paths starting and finishing at the same configuration.

This study aims to improve the rules and regulations system of high-speed rail emergency disposal.

Based on the analysis of the demands, rules and regulations of China concerning on-site high-speed rail emergency disposal, basic principles for revising the regulations on railway technical management (RRTM) are proposed and suggestions and evaluation methods according to the main clauses are put forward.

Basic principles for revising the RRTM are proposed, namely “to meet the actual needs of on-site high-speed railway emergency disposal, standardize the emergency disposal process, improve the efficiency of emergency disposal and keep the consistency between provisions of emergency disposal”. Existing provisions related to emergency disposal efficiency, scenarios, safety and service quality are made up for the deficiencies. To make up for the deficiencies of the existing provisions related to emergency disposal efficiency, improvement of emergency disposal scenarios and guarantee of emergency disposal safety and quality, this paper puts forward suggestions on revising 15 emergency disposal provisions of the RRTM with regard to earthquake monitoring and warning, in-station foreign body invasion warning, air conditioning failure of EMU trains and forced parking of trains in sections. A fuzzy comprehensive evaluation model based on the analytic hierarchy process (AHP) is constructed to evaluate the proposed revision scheme and suggestions, which has been highly recognized by experts.

This study implements the goal of high-quality railway development.

For robot motion planning there exists a large number of different algorithms, each appropriate for a certain domain, and the right choice of planner depends on the specific use case. The purpose of this paper is to consider the application of bin picking and benchmark a set of motion planning algorithms to identify which are most suited in the given context.

The paper presents a selection of motion planning algorithms and defines benchmarks based on three different bin-picking scenarios. The evaluation is done based on a fixed set of tasks, which are planned and executed on a real and a simulated robot.

The benchmarking shows a clear difference between the planners and generally indicates that algorithms integrating optimization, despite longer planning time, perform better due to a faster execution.

The originality of this work lies in the selected set of planners and the specific choice of application. Most new planners are only compared to existing methods for specific applications chosen to demonstrate the advantages. However, with the specifics of another application, such as bin picking, it is not obvious which planner to choose.

A major challenge for mission planning of aircraft is to generate flight paths in highly dynamic environments. This paper presents a new approach for online flight path planning with flight time constraints for fixed-wing UAVs. The flight paths must take into account the kinematic restrictions of the vehicle and be collision-free with terrain, obstacles and no-fly areas. Moreover, the flight paths are subject to time constraints such as predetermined time of arrival at the target or arrival within a specified time interval.

The proposed flight path planning algorithm is an evolution of the well-known RRT* algorithm. It uses three-dimensional Dubins paths to reflect the flight capabilities of the air vehicle. Requirements for the flight time are realized by skillfully concatenating two rapidly exploring random trees rooted in the start and target point, respectively.

The approach allows to consider static obstacles, obstacles which might pop up unexpectedly, as well as moving obstacles. Targets might be static or moving with constantly changing course. Even a change of the target during flight, a change of the target approach direction or a change of the requested time of arrival is included.

The capability of the flight path algorithm is demonstrated by simulation results. Response times of fractions of a second qualify the algorithm for real-time applications in highly dynamic scenarios.

The purpose of this paper is to propose a strategy for the final assembly of helicopter fuselage with weak rigidity parts and mismatched jointing butt ends.

The strategy is based on path planning methods. Compared with traditional path planning methods, the configuration-space and collision detection in the method are different. The obstacles in the configuration-space are weakly rigid and allow continuous contact with the robot. The collision detection is based on interference magnitudes, and the result is divided into no collision, weak collision and strong collision. Only strong collision is unacceptable. Then a compliant jointing path planning algorithm based on RRT is designed, combined with some improvements in search efficiency.

A series of planning results show that the efficiency of this method is higher than original RRT under the same conditions. The effectiveness of the method is verified by a series of simulations and experiments on two sets of systems.

There are few reports on the automation technology of helicopter fuselage assembly. This paper analyzes the problem and provides a solution from the perspective of path planning. This method contains a new configuration-space and collision detection method adapted to this problem and could be intuitive for the jointing of other weakly rigid parts.

This paper aims to identify the variables responsible for classifying the investors into risk takers (RT) and risk avoiders (RA) across their economic perspectives.

The research offers a novel and unobtrusive measure of classifying investors into RT and RA based on a set of financial risk tolerance (FRT) questions. The authors have investigated the causes of discrimination across economic perspectives over a sample of 552 investors exposed to market risk.

The authors identify that out of the total of 11 risk assessment variables, only three are responsible for classifying investors into RA and RT. The variables are risk return trade-off, comfort level dealing with risk, and understanding short-term volatility. Financial literacy is considered as an emerging cause of discrimination. Further, the authors highlight the most striking finding to be the discriminating factors across wealth and source of income of the investors.

Existing research on FRT can be loosely segregated into three groups: the relationship between an individual's financial and non-FRT, estimation of FRT score (FRTS), and perceived self-assessed FRTS. The current research roughly falls into the third category of study where the authors have not only studied the self-assessed risk tolerance but also evaluated the predictors. Most of the studies have focussed on estimating self-assessed FRT with the help of one direct question to the respondent. However, the uniqueness of this study is that the researchers have used an instrument comprising a series of direct and indirect questions that can easily estimate the self-assessed risk perception and also discriminate the role of the economic factors that have any impact on self-assessed FRTS.

Develops a habit‐persistence model which is based on the assumption that experience conditions present behaviour and expectations. Notes that the model combines the adaptive expectations hypothesis with the partial adjustment process. Concludes that accurate forecasts for declining regions are produced but the results for growing regions are not significant.