Task Level Robot Programming using Prioritized Non-Linear Inequality Constraints

abstract

In this paper, we propose a framework for prioritized constraint-based specification of robot tasks. This framework is integrated with a cognitive robotic system based on semantic models of processes, objects, and workcells. The target is to enable intuitive (re-)programming of robot tasks, in a way that is suitable for non-expert users typically found in SMEs. Using CAD semantics, robot tasks are specified as geometric interrelational constraints. During execution, these are combined with constraints from the environment and the workcell, and solved in real-time. Our constraint model and solving approach supports a variety of constraint functions that can be non-linear and also include bounds in the form of inequalities, e.g., geometric interrelations, distance, collision avoidance and posture constraints. It is a hierarchical approach where priority levels can be specified for the constraints, and the nullspace of higher priority constraints is exploited to optimize the lower priority constraints. The presented approach has been applied to several typical industrial robotic use-cases to highlight its advantages compared to other state-of-the-art approaches.