Online Identification for the Automated Threaded Fastening Using GUI Format

Advanced Robotics Systems Scientific Book 200

Understanding Model-Based Reinforcement Learning and its Application in Safe Reinforcement Learning

Model-based reinforcement learning algorithms have been shown to achieve successful results on various continuous control benchmarks, but the understanding of model-based methods is limited. We try to interpret how model-based method works through novel experiments on state-of-the-art algorithms with an emphasis on the model learning part. We evaluate the role of the model learning in policy optimization and propose methods to learn a more accurate model. With a better understanding of model-based reinforcement learning, we then apply model-based methods to solve safe reinforcement learning (RL) problems with near-zero violation of hard constraints throughout training. Drawing an analogy with how humans and animals learn to perform safe actions, we break down the safe RL problem into three stages. First, we train agents in a constraint-free environment to learn a performant policy for reaching high rewards, and simultaneously learn a model of the dynamics. Second, we use model-based methods to plan safe actions and train a safeguarding policy from these actions through imitation. Finally, we propose a factored framework to train an overall policy that mixes the performant policy and the safeguarding policy. This three-step curriculum ensures near-zero violation of safety constraints at all times. As an advantage of model-based method, the sample complexity required at the second and third steps of the process is significantly lower than model-free methods and can enable online safe learning. We demonstrate the effectiveness of our methods in various continuous control problems and analyze the advantages over state-of-the-art approaches

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Collision-free path planning for robots using B-splines and simulated annealing

This thesis describes a technique to obtain an optimal collision-free path for an automated guided vehicle (AGV) and/or robot in two and three dimensions by synthesizing a B-spline curve under geometric and intrinsic constraints. The problem is formulated as a combinatorial optimization problem and solved by using simulated annealing. A two-link planar manipulator is included to show that the B-spline curve can also be synthesized by adding kinematic characteristics of the robot. A cost function, which includes obstacle proximity, excessive arc length, uneven parametric distribution and, possibly, link proximity costs, is developed for the simulated annealing algorithm. Three possible cases for the orientation of the moving object are explored: (a) fixed orientation, (b) orientation as another independent variable, and (c) orientation given by the slope of the curve. To demonstrate the robustness of the technique, several examples are presented. Objects are modeled as ellipsoid type shapes. The procedure to obtain the describing parameters of the ellipsoid is also presented

Real-Time Mapping Using Stereoscopic Vision Optimization

This research focuses on efficient methods of generating 2D maps from stereo vision in real-time. Instead of attempting to locate edges between objects, we make the assumption that the representative surfaces of objects in a view provide enough information to generate a map while taking less time to locate during processing. Since all real-time vision processing endeavors are extremely computationally intensive, numerous optimization techniques are applied to allow for a real-time application: horizontal spike smoothing for post-disparity noise, masks to focus on close-proximity objects, melding for object synthesis, and rectangular fitting for object extraction under a planar assumption. Additionally, traditional image transformation mechanisms such as rotation, translation, and scaling are integrated. Results from our research are an encouraging 10Hz with no vision post processing and accuracy up to 11 feet. Finally, vision mapping results are compared to simultaneously collected sonar data in three unique experimental settings

The UTMOST pulsar timing programme I: overview and first results

We present an overview and the first results from a large-scale pulsar timing programme that is part of the UTMOST project at the refurbished Molonglo Observatory Synthesis Radio Telescope (MOST) near Canberra, Australia. We currently observe more than 400 mainly bright southern radio pulsars with up to daily cadences. For 205 (8 in binaries, 4 millisecond pulsars) we publish updated timing models, together with their flux densities, flux density variability, and pulse widths at 843 MHz, derived from observations spanning between 1.4 and 3 yr. In comparison with the ATNF pulsar catalogue, we improve the precision of the rotational and astrometric parameters for 123 pulsars, for 47 by at least an order of magnitude. The time spans between our measurements and those in the literature are up to 48 yr, which allows us to investigate their long-term spin-down history and to estimate proper motions for 60 pulsars, of which 24 are newly determined and most are major improvements. The results are consistent with interferometric measurements from the literature. A model with two Gaussian components centred at 139 and $463~\text{km} \: \text{s}^{-1}$ fits the transverse velocity distribution best. The pulse duty cycle distributions at 50 and 10 per cent maximum are best described by log-normal distributions with medians of 2.3 and 4.4 per cent, respectively. We discuss two pulsars that exhibit spin-down rate changes and drifting subpulses. Finally, we describe the autonomous observing system and the dynamic scheduler that has increased the observing efficiency by a factor of 2-3 in comparison with static scheduling.Comment: 31 pages, 14 figures, 6 tables, accepted for publication in MNRA