On-line force capability evaluation based on efficient polytope vertex search
This enables real-time force capability evaluation for robot control, addressing a key limitation in robotics for tasks like collaborative manipulation, though it is an incremental improvement over existing polytope methods.
The paper tackles the computational bottleneck of using force/velocity polytopes for real-time robot control by proposing a novel online vertex search algorithm that exploits parallelotope geometry, reducing complexity and enabling a challenging experiment with two robots carrying a 12 kg load to adapt load distribution based on task-space force capabilities.
Ellipsoid-based manipulability measures are often used to characterize the force/velocity task-space capabilities of robots. While computationally simple, this approach largely approximate and underestimate the true capabilities. Force/velocity polytopes appear to be a more appropriate representation to characterize robot's task-space capabilities. However, due to the computational complexity of the associated vertex search problem, the polytope approach is mostly restricted to offline use, e.g. as a tool aiding robot mechanical design, robot placement in work-space and offline trajectory planning. In this paper, a novel on-line polytope vertex search algorithm is proposed. It exploits the parallelotop geometry of actuator constraints. The proposed algorithm significantly reduces the complexity and computation time of the vertex search problem in comparison to commonly used algorithms. In order to highlight the on-line capability of the proposed algorithm and its potential for robot control, a challenging experiment with two collaborating Franka Emika Panda robots, carrying a load of 12 kilograms, is proposed. In this experiment, the load distribution is adapted on-line, as a function of the configuration dependant task-space force capability of each robot, in order to avoid, as much as possible, the saturation of their capacity.