This PhD study aimed to address the sustainability issues of the robotic systems from the environmental and social aspects. During the research, three approaches were developed: the first one an online programming-free model-driven system that utilises web-based distributed human-robot collaboration architecture to perform distant assembly operations. It uses a robot-mounted camera to capture the silhouettes of the components from different angles. Then the system analyses those silhouettes and constructs the corresponding 3D models.Using the 3D models together with the model of a robotic assembly cell, the system guides a distant human operator to assemble the real components in the actual robotic cell. To satisfy the safety aspect of the human-robot collaboration, a second approach has been developed for effective online collision avoidance in an augmented environment, where virtual three-dimensional (3D) models of robots and real images of human operators from depth cameras are used for monitoring and collision detection. A prototype system is developed and linked to industrial robot controllers for adaptive robot control, without the need of programming by the operators. The result of collision detection reveals four safety strategies: the system can alert an operator, stop a robot, move away the robot, or modify the robot’s trajectory away from an approaching operator. These strategies can be activated based on the operator’s location with respect to the robot. The case study of the research further discusses the possibility of implementing the developed method in realistic applications, for example, collaboration between robots and humans in an assembly line.To tackle the energy aspect of the sustainability for the human-robot production environment, a third approach has been developed which aims to minimise the robot energy consumption during assembly. Given a trajectory and based on the inverse kinematics and dynamics of a robot, a set of attainable configurations for the robot can be determined, perused by calculating the suitable forces and torques on the joints and links of the robot. The energy consumption is then calculated for each configuration and based on the assigned trajectory. The ones with the lowest energy consumption are selected.