Search results

This paper aims to propose an online local trajectory planner for safe and fast trajectory generation that combines the jerk-limited trajectory (JLT) generation algorithm and the particle swarm optimization (PSO) algorithm. A trajectory switching algorithm is proposed to improve the trajectory tracking performance. The proposed system generates smooth and safe flight trajectories online for quadrotors.

First, the PSO algorithm method can obtain the optimal set of target points near the path points obtained by the global path searching. The JLT generation algorithm generates multiple trajectories from the current position to the target points that conform to the kinetic constraints. Then, the generated multiple trajectories are evaluated to pick the obstacle-free trajectory with the least cost. A trajectory switching strategy is proposed to switch the unmanned aerial vehicle (UAV) to a new trajectory before the UAV reaches the last hovering state of the current trajectory, so that the UAV can fly smoothly and quickly.

The feasibility of the designed system is validated through online flight experiments in indoor environments with obstacles.

The proposed trajectory planning system is integrated into a quadrotor platform. It is easily implementable onboard and computationally efficient.

The proposed local planner for trajectory generation and evaluation combines PSO and JLT generation algorithms. The proposed method can provide a collision-free and continuous trajectory, significantly reducing the required computing resources. The PSO algorithm locally searches for feasible target points near the global waypoint obtained by the global path search. The JLT generation algorithm generates trajectories from the current state toward each point contained by the target point set. The proposed trajectory switching strategy can avoid unnecessary hovering states in flight and ensure a continuous and safe flight trajectory. It is especially suitable for micro quadrotors with a small payload and limited onboard computing power.

This paper aims to propose an environmentally adaptive trajectory planning system considering the dynamic characteristics of unmanned aerial vehicles (UAVs) and the distance between obstacles and the UAV. The system generates a smooth and safe flight trajectory online.

First, the hybrid A* search method considering the dynamic characteristics of the quadrotor is used to find the collision-free initial trajectory. Then, environmentally adaptive velocity cost is designed for environment-adaptive trajectory optimization using environmental gradient data. The proposed method adaptively adjusts the autonomous flight speed of the UAV. Finally, the initial trajectory is applied to the multi-layered optimization framework to make it smooth and dynamically viable.

The feasibility of the designed system is validated by online flight experiments, which are in unknown, complex situations.

The proposed trajectory planning system is integrated into a vision-based quadrotor platform. It is easily implementable onboard and computationally efficient.

A hybrid A* path searching method is proposed to generate feasible motion primitives by dispersing the input space uniformly. The proposed method considers the control input of the UAV and the search time as the heuristic cost. Therefore, the proposed method can provide an initial path with the minimum flying time and energy loss that benefits trajectory optimization. The environmentally adaptive velocity cost is proposed to adaptively adjust the flight speed of the UAV using the distance between obstacles and the UAV. Furthermore, a multi-layered environmentally adaptive trajectory optimization framework is proposed to generate a smooth and safe trajectory.

The gender-gap reversal in education could have far-reaching consequences for marriage and family lives in the United States. This study seeks to address the following question: As women increasingly marry men with less education than they have themselves, is the traditional male breadwinner model in marriage challenged?

This study takes a life course approach to examine how educational assortative mating shapes trajectories of change in female breadwinning status over the course of marriage. It uses group-based trajectory models to analyze data from the National Longitudinal Survey of Youth 1979.

The results reveal substantial movement by wives in and out of the primary breadwinner role across marital years and great heterogeneity in female breadwinning trajectories across couples. In addition, educational assortative mating plays a role in shaping female breadwinning trajectories: Compared with wives married to men whose educational levels equal or exceed their own, wives married to men with less education than themselves are more likely to have a continuously high probability of being primary earners and are also more likely to gradually or rapidly transition into primary earners if initially they are not.

This study examines couples’ breadwinning arrangements over an extended period of time and identifies qualitatively distinct patterns of change in female breadwinning that are not readily identifiable using ad hoc, ex ante classification rules. The findings suggest that future research on the economics of marriage and couple relations in families would benefit from a life course approach to conceptualizing couples’ dynamic divisions of breadwinning.

This contribution introduces sequence analysis to higher education research, an explorative technique aiming at detecting patterns, regularities and resemblance in time-ordered data. Thereby, it enables a holistic perspective on over-time developments and processes such as educational pathways or academic careers. In this contribution, the foundations and general logic of sequence analysis will be described. As an example, referring to the life course as a framing paradigm, sequence analysis is applied to reconstruct the study trajectories of a cohort of bachelor students in Germany. The potential of sequence analysis in three specific higher education research areas is outlined, that is, to study post-secondary education trajectories, academic careers and the development trajectories of higher education organizations. The conclusion discusses advantages and disadvantages, challenges and practicalities.

Collaboration and acquisition have traditionally been observed as two alternative strategies when accessing external technologies. However, real option scholars have recently argued that firms can also engage in transitional technology sourcing trajectories where collaboration and acquisition are used as complementary strategies. While these real option scholars have identified factors that influence when partners are likely to shift from collaboration to acquisition, they remain silent on how such a transition can be effectively managed. Based on a multiple case study of four transitional technology sourcing trajectories between one new entrepreneurial and one established firm, this study therefore explores how the pre-acquisition collaboration stage and the post-acquisition integration are related to each other. Findings suggest that entrepreneurial companies may use the pre-acquisition collaboration stage as a period to evaluate the goodwill of the established partner. In addition, we point to the presence of pre-acquisition integration efforts and the extent of strategic convergence during the pre-acquisition collaboration stage as factors that substantially influence the success of the post-acquisition integration process in transitional governance trajectories.

In order to deepen understanding of team processes in dynamic organizational contexts, we suggest that analyses employing techniques to identify and analyze team member interaction patterns and trajectories are necessary. After presenting a brief review of interaction data coding and reliability requirements, we first review examples of two approaches used in the identification and analysis of interaction patterns in teams: lag sequential analysis and T-pattern analysis. We then describe and discuss three statistical techniques used to analyze team interaction trajectories: random coefficient modeling, latent growth modeling, and discontinuous growth analysis. We close by suggesting several ways in which these techniques could be applied to data analysis in order to expand our knowledge of team interaction, processes, and outcomes in complex and dynamic settings.

The purpose of this paper is to enhance control accuracy, energy efficiency and productivity of customized industrial robots by the proposed multi-objective trajectory optimization approach. To obtain accurate dynamic matching torques of the robot joints with optimal motion, an improved dynamic model built by a novel parameter identification method has been proposed.

This paper proposes a novel multi-objective optimal approach to minimize the time and energy consumption of robot trajectory. First, the authors develop a reliable dynamic parameters identification method to obtain joint torques for formulating the normalized energy optimization function and dynamic constraints. Then, optimal trajectory variables are solved by converting the objective function into relaxation constraints based on second-order cone programming and Runge–Kutta discrete method to reduce the solving complexity.

Extensive experiments via simulation and in real customized robots are conducted. The results of this paper illustrate that the accuracy of joint torque predicted by the proposed model increases by 28.79% to 79.05% over the simplified models used in existing optimization studies. Meanwhile, under the same solving efficiency, the proposed optimization trajectory consumes a shorter time and less energy compared with the existing optimization ones and the polynomial trajectory.

A novel time-energy consumption optimal trajectory planning method based on dynamic identification is proposed. Most existing optimization methods neglect the effect of dynamic model reliability on energy efficiency optimization. A novel parameter identification approach and a complete dynamic torque model are proposed. Experimental results of dynamic matching torques verify that the control accuracy of optimal robot motion can be significantly improved by the proposed model.

The purpose of this paper is to develop a real-time trajectory planner with optimal maneuver for autonomous vehicles to deal with dynamic obstacles during parallel parking.

To deal with dynamic obstacles for autonomous vehicles during parking, a long- and short-term mixed trajectory planning algorithm is proposed in this paper. In long term, considering obstacle behavior, A-star algorithm was improved by RS curve and potential function via spatio-temporal map to obtain a safe and efficient initial trajectory. In short term, this paper proposes a nonlinear model predictive control trajectory optimizer to smooth and adjust the trajectory online based on the vehicle kinematic model. Moreover, the proposed method is simulated and verified in four common dynamic parking scenarios by ACADO Toolkit and QPOASE solver.

Compared with the spline optimization method, the results show that the proposed method can generate efficient obstacle avoidance strategies, safe parking trajectories and control parameters such as the front wheel angle and velocity in high-efficient central processing units.

It is aimed at improving the robustness of automatic parking system and providing a reference for decision-making in a dynamic environment.

The paper aims to reduce the low-frequency resonance and residual vibration of the robot during the operation, improve the working accuracy and efficiency. A reduced weight and large load-to-weight ratio can improve the practical application of a collaborative robot. However, flexibility caused by the reduced weight and large load-to-weight ratio leads to low-frequency resonance and residual vibration during the operation of the robot, which reduces the working accuracy and efficiency. The vibrations of the collaborative robot are suppressed using a modified trajectory-planning method.

A rigid-flexible coupling dynamics model of the collaborative robot is established using the finite element and Lagrange methods, and the vibration equation of the robot is derived. Trajectory planning is performed with the excitation force as the optimization objective, and the trajectory planning method is modified to reduce the vibration of the collaborative robot and ensure the precision of the robot terminal.

The vibration amplitude is reduced by 80%. The maximum torque amplitude of the joint before the vibration suppression reaches 50 N·m. After vibration suppression, the maximum torque amplitude of the joint is 10 N·m, and the resonance phenomenon is eliminated during the operation process. Consequently, the effectiveness of the modified trajectory planning method is verified, where the vibration and residual vibration in the movement of the collaborative robot are significantly reduced, and the positioning accuracy and working efficiency of the robot are improved.

This method can greatly reduce the vibration and residual vibration of the collaborative robot, improve the positioning accuracy and work efficiency and promote the rapid application and development of collaborative robots in the industrial and service fields.