New paradigms in industrial robotics no longer require physical separation between robotic manipulators and humans. Moreover, in order to optimize production, humans and robots are expected to collaborate to some extent. In this scenario, involving a shared environment between humans and robots, common motion generation algorithms might turn out to be inadequate for this purpose. This paper proposes a kinematic control strategy which enforces safety, while maintaining the maximum level of productivity of the robot. The resulting motion of the (possibly redundant) robot is obtained as an output of an optimization-based real-time algorithm in which safety is regarded as a hard constraint to be satisfied. The methodology is experimentally validated on a dual-arm concept robot with 7-DOF per arm performing a manipulation task.

Industrial robots are today separated from human workers by means of safety barriers, that protect humans from the risk of collisions. This separation has a clear negative influence on diffusion of robotic technology in shopfloors. On the other hand the removal of protective barriers gives rise to safety issues, that can be addressed with a combination of approaches, including sensor based reactive control. In this paper a distributed proximity sensor, to be mounted on the links of the manipulator, is presented. The optimal placement of the spots of such sensor is discussed, taking into account detection capabilities and safety enhancement. Experiments developed on an ABB IRB 140 robot using off-the-shelf infrared distance sensors as spots are presented.

Tight human-robot interaction and collaboration will characterize future robot tasks. Robot working environments will be increasingly unstructured, as safety barriers will be removed to allow a continuous cooperation of robotic and human workers. Such a working scenario calls for novel safety systems capable of combining productivity with workers' safety. In this paper, a method for the definition of a task-consistent collision avoidance safety strategy is presented. A classification of task constraints based on relevance for task completion is introduced. Control of task constraints enforcement is performed through a state machine. A template for such state machine is proposed. Experimental validation of the proposed safety system on a dual-arm industrial robot prototype is presented.