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In this paper, the traditional shortest path planning problem for vehicle is advanced to length-targeted path planning problem, i.e., to plan path with its length being as close to a specified value as possible. Lengthening and shortening of given initial paths are used to solve this problem. Based on an operation set consisting of three basic path homotopies, we build a comprehensive and systematic framework to plan paths with target length, which is a generalization of the existing related studies. Thereby, the expected paths can be independently searched through such deformation processes within topological classes. The proposed framework can produce the largest length coverage in different scenarios and under different conditions, and it can also generate expected paths with arbitrary topological classification in terms of the curvature constraint and the obstacle constraint. Examples show that our lengthening and shortening method can effectively solve the length-targeted path planning problem in environment without or with obstacles. Note to Practitioners —This paper presents a curvature-bounded lengthening and shortening approach for path planning of vehicles. The curvature and obstacle constraints in the path planning problem are handled using the topological technique. In addition, a rigorous classification system is presented to systematically categorize the potential paths. Within a path class, any path can be deformed to other paths while keeping the curvature bounded. Then, the path deformation process is conducted via a series of basic homotopy operations to approach the target length. The length coverage range of this process is reduced when some endpoint conditions are meet or the vehicle is trapped into a closed region.

The ability to deploy multiple unmanned aerial vehicles expands their application range, but aerial recovery of unmanned aerial vehicles presents many unique challenges owing to the number of unmanned aerial vehicles and the limited recovery time. In this paper, scheduling the aerial recovery of multiple unmanned aerial vehicles by one mothership is posed as a combinatorial optimization problem. A mathematical model with recovery time windows of the unmanned aerial vehicles is developed to formulate this problem. Furthermore, a genetic algorithm is proposed for finding the optimal recovery sequence. The algorithm adopts the path representation of chromosomes to simplify the encoding process and the genetic operations. It also resolves decoding difficulties by iteration, and thus can efficiently generate a recovery timetable for the unmanned aerial vehicles. Simulation results in stochastic scenarios validate the performance of the proposed algorithm compared with the random search algorithm and the greedy algorithm.

In this paper, we propose an iterative strategy to enhance the performance of task assignment and path planning in applications of distributed multiple unmanned aerial vehicles (Multi-UAV). As an improvement of the conventional management of airborne computation and communication resources of UAVs, our strategy overcomes difficulties caused by the information coupling between task assignment and path planning. A distributed mission planning framework is presented with the strategy, in which the UAVs re-evaluate unreasonable assignment results and overvalued tasks during the planning process. The proposed strategy has advantages in algorithm stability and complexity, as it controls the task valuation error within a certain range via computation with limited complexity. Compared to the conventional methods, our strategy with the framework can achieve better performance of planning results and consume less computing resources. Simulation results show the effectiveness of the proposed strategy in terms of the computational efficiency and the mission execution reward.

To maximize the advantages of being low-cost, highly mobile, and having a high flexibility, aerial recovery technology is important for unmanned aerial vehicle (UAV) swarms. In particular, the operation mode of &ldquo, launch-recovery-relaunch&rdquo, will greatly improve the efficiency of a UAV swarm. However, it is difficult to realize large-scale aerial recovery of UAV swarms because this process involves complex multi-UAV recovery scheduling, path planning, rendezvous, and acquisition problems. In this study, the recovery problem of a UAV swarm by a mother aircraft has been investigated. To solve the problem, a recovery planning framework is proposed to establish the coupling mechanism between the scheduling and path planning of a multi-UAV aerial recovery. A genetic algorithm is employed to realize efficient and precise scheduling. A homotopic path planning approach is proposed to cover the paths with an expected length for long-range aerial recovery missions. Simulations in representative scenarios validate the effectiveness of the recovery planning framework and the proposed methods. It can be concluded that the recovery planning framework can achieve a high performance in dealing with the aerial recovery problem.

Bounded curvature path planning with specified length is a significant technique for single-vehicle assignments and multi-vehicle coordination, especially in the real-time applications. Any existed path can be reached from the curvature-bounded deformation of some other paths. Based on that, this paper proposes a structured homotopy-based (SH-based) planner as an improvement of the conventional path elongation strategy. Not alike path elongation which is driven by initial path, the proposed planner is driven by endpoint condition. It searches expected path within the synthesis of several path homotopies, therefore it has stronger flexibility and adaptability for different endpoint condition than simple elongation. Special homotopy structures which ensure the monotonicity of path length with respect to the homotopy parameter are employed for implementation of fast solution searching. In addition, the proposed method is extended from the point target case to the manifold target case, and the target point transference strategy is adopted to make a trade-off between the path length deviation and the target position value. Static and dynamic cases are respectively simulated for an example of Dubins vehicle, which validate the performance of the proposed method.

Condition monitoring, as part of the intelligent infrastructure concept, can significantly improve the reliability, safety and efficiency of rail operations. Degradation in infrastructure can be detected before a problem occurs, without interrupting normal operations. This paper contributes to the development of analytics tools and methods for condition monitoring using sensor data. A data-driven method is proposed to monitor the wheel wear of high-speed trains using onboard vibration sensors. In this method, a number of signal processing techniques and statistical methods are applied and extended to extract useful information from multi-location vibration data and estimate the wheel wear. We test the method using real operational data collected from high-speed trains in China over half a year. Preliminary results show that the method is accurate and can be easily applied to real world operations. The method can be extended to provide fault or degradation predictions, which reduces the in-service failures, and enables predictive maintenance.

External perturbations and actuator faults are two practical and significant issues that deserve designers' considerations when synthesizing the controllers for spacecraft rendezvous. A composite robust fault-tolerant control (FTC) scheme that does not require the fault information is proposed in this paper for limited-thrust rendezvous in near-circular orbits. Within the control scheme, a reliable integral sliding mode (ISM) auxiliary controller and a modified guaranteed cost FTC are, respectively, developed to attenuate the external disturbances and to stabilize the nominal rendezvous system with actuator faults. Comparisons with previous works as well as a more practical and challenging simulation example are presented to verify the advantages of this composite control scheme.

An adaptive sliding mode control scheme is proposed for spacecraft rendezvous constrained by the external perturbations, the non-circularity of reference orbit, and the time-varying saturation, which can be induced by the temporal characteristics and the accidental faults of actuators. With some reasonable assumptions, we prove that the adaptive control scheme can achieve asymptotic stability without using the effectiveness information of actuators. Compared with the existing results, numerical experiment with longer initial distances, lower saturation bounds, and better performance is achieved within a nonlinear two-body system.

To improve the overall performance of mission planning for the unmanned aerial vehicles (UAVs), a multistage path prediction (MPP) for trajectory generation with rendezvous is addressed in this paper. The proposed real-time algorithm consists of four stages: path estimation, path planning, flyable trajectory generation, and trajectory modification for rendezvous. In every planning horizon, each UAV utilizes the local A* algorithm to estimate all probable paths and then the results serve as input for the task assignment system. A simple assignment algorithm is briefly introduced to validate the effectiveness of MPP. Based on the assignment, the polygonal paths are further obtained by using the global A* algorithm. Then these paths are smoothed to be flyable by using the cubic b-spline curve. In the last stage, the trajectories are modified for rendezvous of the UAVs to execute specific many-to-one tasks. Results of different stages are continuously revised and delivered to the task assignment syste...

In the dynamical environment with forbidden areas, the achievement of multiple unmanned aerial vehicles (UAVs) executing cooperative missions strongly depends on the validity of mission planning results, which may be affected by the error of flight time estimation. In this paper, a hierarchical path generation scheme for improving the adaptability and performance of the distributed mission planning (DMP) system of the UAVs is proposed. The effectiveness of the market-based task allocation is enhanced by conducting a rough path planning beforehand, where only the neighboring forbidden areas of the vehicles are considered for a tradeoff between the planning accuracy and the computation. The subsequent refined path planning, which provides the guidance information to the control system, is also presented to assess the precision level of the rough path planning and the mission execution reward of the UAV fleet. The simulation results demonstrate the advantages and effectiveness of the proposed method for multiple UAVs executing missions in stochastic scenarios.