Browsing by Subject "Trajectory planning"
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Item Assessment of a velocity-based robot motion planner for surface preparation with geometric uncertainty(2022-08-12) Kesler, Dalton; Pryor, Mitchell Wayne; Landsberger, SheldonThis effort reviews the efficacy of a novel robot planning method for surface preparation. The method uses virtual fixtures to aid robot operators by automating trajectory generation for interaction with task surfaces. By providing a manipulable representation of the task surface, a list of associated interaction poses for the robot, and an adaptive trajectory for maximal surface coverage, the method effectively alleviates the burden of managing low-level system resources, allowing the operator to focus on high-level control and task execution. A series of auxiliary algorithms are developed for task-specific implementation, and the method is tested using a series of experimental cases analogous to surface preparation tasks such as polishing, cleaning, or inspection. In these experimental cases, the method proved useful for noncontact scanning and inspection tasks as well as a simple contact task. The major demonstrated benefits include, automated trajectory generation given task parameters, identification of workpiece defects using a depth camera and a newly created filtering algorithm, and real-time trajectory adjustment corresponding to identified defects.Item On-demand planning of a school of autonomous mobile robots for prioritized task completion(2020-05-06) Bakshi, Soovadeep; Chen, Dongmei, Ph. D.; Beaman, Joseph J; Longoria, Raul G; Tanaka, Takashi; Hanasusanto, Grani AUsing autonomous mobile robots (AMRs) to collaboratively complete tasks has been intensively pursued by both industry and academia. Despite advancements in the field of AMR operation, there are still many challenges associated with this domain. For instance, to optimally operate numerous AMRs to collaboratively complete a large number of tasks in real-time is highly challenging. First, prioritized tasks need to be continuously and optimally assigned to the entire school of AMRs. When the number of AMRs is larger than 20 and the number of tasks is greater than 500, the computational cost of optimally assigning tasks to each AMR and scheduling these AMRs to complete the assigned tasks is significant, which renders most operations of such an AMR system infeasible in real time. Secondly, for a typical field equipped with AMRs, the tasks involve going from one location to another, making the search space asymmetric. The AMR needs to travel from its starting location to its ending location to complete a task. Thus, this AMR problem is operating in the ‘task space’ instead of the actual ‘point space’, i.e., the tasks are assigned to individual AMRs without repetitions instead of the points/locations in the field. This aspect will dramatically increase the complexity of the optimization process and traditional ‘point space’ methods might not apply. Thirdly, the tasks might have different priority levels. The fact that completing tasks with higher priorities earlier is preferred during clustering and scheduling adds another dimension to the optimization process, which can be defined as an asymmetric ‘priority space’. The asymmetricity of the ‘priority space’ also affects the clustering and scheduling of the AMRs. There has been extensive research on scheduling of AMRs to complete tasks by traveling to specific locations. However, most algorithms consider that the tasks do not have priorities. The ‘priority space’ necessitates exploring new methods for optimal scheduling in an asymmetric space. Finally, the clustering and scheduling of the school of AMRs should be adapted to operational and environmental changes. For instance, a battery-powered AMR will need to efficiently utilize its energy, and therefore, energy conscientious trajectory generation as well as optimal recharge scheduling of AMRs under such circumstances is also of great interest. The overall planning process should be completed in a timely manner such that the operation of the school of AMRs can be implemented in real time, i.e., on-demand. In order to address the above challenges, this research will develop algorithms for real-time control of a school of AMRs that can optimally cluster the AMRs with tasks having various levels of priorities, schedule each AMR to complete the tasks with the highest efficiency, generate energy-efficient trajectories for each AMR, and perform energy-based recharge scheduling.Item Velocity-based robot motion planner for under-constrained trajectories with part-specific geometric variances(2021-12-03) Oridate, Ademola Ayodeji; Seepersad, Carolyn; Pryor, Mitchell Wayne; Crawford, Richard H; O'Brien, William JIndustrial manipulators often interact with large and complex objects for a variety of automation tasks. Finding a feasible path for the robot end-effector that ensures task success is often non-trivial due to considerations such as reachability, singularity avoidance, and collision avoidance. This dissertation presents an approach to expand the search space for feasible robot trajectories (and search for an optimal solution) around large and complex geometry by taking advantage of task redundancy for certain tasks without compromising task objectives. The effort builds on previous work enabling virtual fixture generation for complex shapes given CAD or scan data. While existing planners found in the literature have successfully implemented redundancy resolution techniques, the focus has mostly been on the execution of fully constrained trajectories by robots with Degrees of Freedom (DOF) greater than the number of task variables. This effort has successfully extended this discussion to address situations in which the nature of the task allows for relaxation of both geometric and non-geometric task variables to enable the exploration of a larger range of feasible solutions. The effort was developed into a trajectory planning library on the ROS (Robot Operating System) framework and tested by simulating an interaction of a six-axis industrial robot with an F-16 aircraft and a conformal Additive Manufacturing (AM) case study. The results show benefits such as increased task surface coverage with minimal robot base placements and greater user control over the trajectory generation since the user can easily identify problematic points in the trajectory and specify parameters to modify them in real-time.