Direct methods for vision-based robot control : application and implementation

With the growing interest of integrating robotics into everyday life and industry, the requirements towards the quality and quantity of applications grows equally hard. This trend is profoundly recognized in applications involving visual perception. Whereas visual sensing in home environments tend to be mainly used for recognition and localization, safety becomes the driving factor for developing intelligent visual control algorithms. More specifically, a robot operating in a human environment should not collide with obstacles and executed motion should be as smooth as possible. Furthermore, as the environment is not known on beforehand, a high demand on the robustness of visual processing is a necessity. On the other hand, in an industrial setting, the environment is known on beforehand and safety is mainly guaranteed by excluding a human operator. Despite these reasons, and the fact that visual servoing has gained much attention from industry to become a standard solution for robotic automation tasks, applications are highly simplified. For example, methods such as visual fault detection are already a mature technique in industrial manufacturing, where a fixed camera observes a product (e.g., on a conveyor belt) and checks whether it meets certain requirements. These operations can be executed at a fairly high rate due to the simplicity of the system (e.g., static camera) and the simplification of the processing task (e.g., binary images). For both areas the identified difficulties are similar. Foremost, this is the slow nature of (robust) visual processing, in respect to the ever growing demand of increasing speed and reducing delay. These two application areas with analogous limitations motivate the design of more direct approaches of vision in visual control systems. Therefore, in order to meet the requirements for next generation visual control systems, this thesis presents approaches which employ visual measurements as a direct feedback to design constrained motion. First, for industrial robotics, in order to obtain the required positioning accuracy, the measurement and fixation system have to be highly rigid and welldesigned, implying high cost and long design time. By measuring the position of objects directly with a camera, instead of indirectly by motor encoders, the requirements of the measurement and fixation system are less demanding. Moreover, this motivates the miniaturization of the complete control system. The approach is validated in experiments on a simplified 2D planar stage (i.e., considerable friction, poor fixation), which attains similar performance compared to encoder-based positioning systems. Secondly, in a human-centred environment, this direct sensing can improve traditional visual control systems, when subject to certain disturbances. More specifically, a method is proposed that uses an image-based feedforward controller on top of traditional position-based visual servo control to overcome disturbances such as friction or poorly designed local motor controllers. This visual feedforward control action is only active when an image-based error is present and vanishes when that error goes to zero. The method is validated on an anthropomorphic robotic manipulator with 7 degrees of freedom, intended for operation in the human care environment. Third, sensing the product directly gives rise to designing motion directly. Whereas in traditional approaches the motion trajectory is designed offline and can not be changed at runtime, direct trajectory generation computes the motion of the next step based on current state and events. This means that at any instance in time, the trajectory of a motion system can be altered with respect to certain desired kinematic or dynamic constraints. For industrial applications this makes manufacturing on near-repetitive or non-rigid structures (e.g. flexible displays) possible. When applied to a robotic manipulator, this enables obstacle avoidance to no longer be on path planning level, but on trajectory planning level, where kinematic or dynamic constraints can be taken into account. This results in a motion that is smoother than when obstacle avoidance with path planning is employed. For both application areas this direct trajectory generation method is implemented and shows high flexibility in constrained motion trajectory design.