Conforming to safety standards often limits collaborative robots' performance and size, restricting their applications despite their capabilities. Planning their motions in human environments involves a trade-off between optimal trajectory planning and quick adaptation to dynamic, unstructured spaces. Traditional trajectory planning methods either use simplified robot models and sacrifice robot's abilities for computational efficiency, or exploit robots' abilities fully but have high computational complexity and rely on substantial pre-computation. This paper introduces an approach for trajectory planning that exploits robot's full motion abilities while planning on-the-fly. In each step of the trajectory execution, it evaluates robot's movement ability using polytope algebra and calculates a time-optimal Trapezoidal Acceleration Profile (TAP) on the remaining trajectory. The method is shown to be near time-optimal (around 5% slower trajectories) by benchmarking it against the state-of-the-art time-optimal method TOPP-RA. The method allows reaching higher velocities (able to plan up to 100% of the robot's kinematic limits) while at the same time lowering the tracking error (under 4mm) than traditional Cartesian Space planning methods. A mock-up experiment demonstrates its efficiency in collaborative waste sorting using a Franka Emika Panda robot.

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 the 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 parallelotope 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.

Cybersecurity is of vital importance to industrial control systems (ICSs), such as ship automation, manufacturing, building, and energy automation systems. Many control applications require hard real-time channels, where the delay and jitter are in the levels of milliseconds or less. To the best of our knowledge, no encryption algorithm is fast enough for hard real-time channels of existing industrial fieldbuses and, therefore, made mission-critical applications vulnerable to cyberattacks, e.g., delay and data injection attacks. In this article, we propose a novel recursive watermark (RWM) algorithm for hard real-time control system data integrity validation. Using a watermark key, a transmitter applies watermark noise to hard real-time signals and sends through the unencrypted hard real-time channel. The same key is transferred to the receiver by the encrypted nonreal-time channel. With the same key, the receiver can detect if the data have been modified by the attackers and take action to prevent catastrophic damages. We provide analysis and methods to design proper watermark keys to ensure reliable attack detection. We use a ship propulsion control system for the simulation-based case study, where our algorithm smoothly shuts down the system after attacks. We also evaluated the algorithm speed on a Siemens S7-1500 programmable logic controller (PLC). This hardware experiment demonstrated that the RWM algorithm takes about 2.8 μs to add or validate the watermark noise on one sample data point. As a comparison, common cryptic hashing algorithms can hardly process a small data set under 100 ms. The proposed RWM is about 32 to 1375 times faster than the standard approaches.