RESUMO
In this paper, a procedure for experimental optimization under safety constraints, to be denoted as constraint-aware Bayesian Optimization, is presented. The basic ingredients are a performance objective function and a constraint function; both of them will be modeled as Gaussian processes. We incorporate a prior model (transfer learning) used for the mean of the Gaussian processes, a semi-parametric Kernel, and acquisition function optimization under chance-constrained requirements. In this way, experimental fine-tuning of a performance objective under experiment-model mismatch can be safely carried out. The methodology is illustrated in a case study on a line-follower application in a CoppeliaSim environment.
RESUMO
Contact driven tasks, such as surface conditioning operations (wiping, polishing, sanding, etc.), are difficult to program in advance to be performed autonomously by a robotic system, specially when the objects involved are moving. In many applications, human-robot physical interaction can be used for the teaching, specially in learning from demonstrations frameworks, but this solution is not always available. Robot teleoperation is very useful when user and robot cannot share the same workspace due to hazardous environments, inaccessible locations, or because of ergonomic issues. In this sense, this article introduces a novel dual-arm teleoperation architecture with haptic and visual feedback to enhance the operator immersion in surface treatment tasks. Two task-based assistance systems are also proposed to control each robotic manipulator individually. To validate the remote assisted control, some usability tests have been carried out using Baxter, a dual-arm collaborative robot. After analysing several benchmark metrics, the results show that the proposed assistance method helps to reduce the task duration and improves the overall performance of the teleoperation.
