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Checking the Results in Coplanar Collision Analysis
Published in Donald E. Struble, John D. Struble, Automotive Accident Reconstruction, 2020
Donald E. Struble, John D. Struble
The trajectory analysis provides the distance in each segment, the path angle in the segment, the vehicle rotation during the segment, the computed average crab angle, the average deceleration, and the time in the segment. The vehicle rotation should be checked to see if it is more than 90° between key points.
Robotics
Published in Jian Chen, Bingxi Jia, Kaixiang Zhang, Multi-View Geometry Based Visual Perception and Control of Robotic Systems, 2018
Jian Chen, Bingxi Jia, Kaixiang Zhang
In robotics, a path is a spatial concept in space that leads from an initial pose to a final pose. A trajectory is a path with specified time constraints, i.e., it describes the poses at each specified time instants.
Speed profile optimisation for intelligent vehicles in dynamic traffic scenarios
Published in International Journal of Systems Science, 2020
Zhuoyang Du, Dong Li, Kaiyu Zheng, Shan Liu
Generally, a trajectory consists of a path in Cartesian space and a speed profile in terms of executing time. Many works concentrate on the direct optimisation of trajectory which is parameterised by time (Ziegler et al., 2014). Normally, the trajectory optimisation problem is non-convex and solved by numerical optimisation to find local optimums. The parameter space of trajectory is high dimensional and great efforts are needed in the optimisation process. In Ziegler and Stiller (2009), the trajectory is planned within a geometric acyclic graph which is established by sampling deterministically from the spatiotemporal state lattices. Similarly, McNaughton et al. (2011) propose a spatio-temporal searching graph to explore both spatial and temporal dimensions. Specially, the algorithm implements some high-level manoeuvre decisions such as lane changes, and car following by low-level trajectory planner. Schwesinger et al. (2013) present a sampling-based partial motion planning framework with a particular vehicle state/time pair representing the sampling node. With a rich set of feasible trajectories generated, an evaluation process is applied to get the best one.
Using collaborative robots to assist with travel path development for material deposition based additive manufacturing processes
Published in Computer-Aided Design and Applications, 2018
The Baxter robot [3] has seven joints each, for a left and right arm (Appendix A). The kinematic model has been previously developed, and for a detailed analysis, refer to e Silva et al [8]. This unified model contains seven reconfiguration parameters: K1, K2, K3, K4, K5, K6, and K7 (Tab. 2). Using kinematic theory, the Baxter joint values are converted to the World Frame (points’ position and orientation). A sample of trajectory points are presented in Appendix B. Using the Matlab tools, the trajectory has been validated and visualized.
A methodology for generating driving styles for autonomous cars
Published in Journal of Intelligent Transportation Systems, 2022
Rafael Peralta, Israel Becerra, Ubaldo Ruiz, Rafael Murrieta-Cid
The proposed definition of driving styles uses a series of compositions between different spaces, which we introduce below. First, assume a series of systems for a given vehicle, e.g., a velocity controller, whose behavior is defined by a tuple of parameters. Let be the parameters space defined as the set of all such tuples of parameters. According to a tuple in the vehicle can carry out a set of trajectories in the trajectory space Σ. A trajectory is a time-parameterized function that maps a path in the state space The state is defined as a tuple that might include position, orientation, velocity, acceleration, etc. Upon thinking of a human subject on board the vehicle, the execution of different trajectories becomes a driving pattern. Humans perceive motion through their senses, for example, optical flow using the eyesight or acceleration changes using the vestibular system. Thus, consider a functional that maps a given trajectory to a percept, that is, a mental recreation of the stimulus. The series of compositions mentioned above go from a tuple p in the parameter space to trajectories σ in the state space Σ, which in turn will evoke perceptual phenomena in the passenger through the functional that maps trajectories to percepts. See Figure 1. Finally, let H(p) be a function that considers all such compositions and maps from a parameter tuple to a percept in the human subject.