In this paper, we study a navigation problem where a mobile robot needs to locate a mmWave wireless signal. Using the directionality properties of the signal, we propose an estimation and path planning algorithm that can efficiently navigate in cluttered indoor environments. We formulate Extended Kalman filters for emitter location estimation in cases where the signal is received in line-of-sight or after reflections. We then propose to plan motion trajectories based on belief-space dynamics in order to minimize the uncertainty of the position estimates. The associated non-linear optimization problem is solved by a state-of-the-art constrained iLQR solver. In particular, we propose a method that can handle a large number of obstacles (∼ 300) with reasonable computation times. We validate the approach in an extensive set of simulations. We show that our estimators can help increase navigation success rate and that planning to reduce estimation uncertainty can improve the overall task completion speed.

Symbolic task planning for robots is computationally challenging due to the combinatorial complexity of the possible action space. This fact is amplified if there are several sub-goals to be achieved due to the increased length of the action sequences. In this work, we propose a multi-goal symbolic task planner for deterministic decision processes based on Monte Carlo Tree Search. We augment the algorithm by prioritized node expansion which prioritizes nodes that already have fulfilled some sub-goals. Due to its linear complexity in the number of sub-goals, our algorithm is able to identify symbolic action sequences of 145 elements to reach the desired goal state with up to 48 sub-goals while the search tree is limited to under 6500 nodes. We use action reduction based on a kinematic reachability criterion to further ease computational complexity. We combine our algorithm with object localization and motion planning and apply it to a real-robot demonstration with two manipulators in an industrial bearing inspection setting.

Least-squares programming is a popular tool in robotics due to its simplicity and availability of open-source solvers. However, certain problems like sparse programming in the$\ell_{0}$- or$\ell_{0}-\mathbf{norm}$for time-optimal control are not equivalently solvable. In this work, we propose a non-linear hierarchical least-squares programming (NL-HLSP) for time-optimal control of non-linear discrete dynamic systems. We use a continuous approximation of the heaviside step function with an additional term that avoids vanishing gradients. We use a simple discretization method by keeping states and controls piece-wise constant between discretization steps. This way, we obtain a comparatively easily implementable NL-HLSP in contrast to direct transcription approaches of optimal control. We show that the NL-HLSP indeed recovers the discrete time-optimal control in the limit for resting goal points. We confirm the results in simulation for linear and non-linear control scenarios.

Real-world applications require light-weight, energy-efficient, fully autonomous robots. Yet, increasing autonomy is oftentimes synonymous with escalating computational requirements. It might thus be desirable to offload intensive computation-not only sensing and planning, but also low-level whole-body control-to remote servers in order to reduce on-board computational needs. Fifth Generation (5G) wireless cellular technology, with its low latency and high bandwidth capabilities, has the potential to unlock cloud-based high performance control of complex robots. However, state-of-the-art control algorithms for legged robots can only tolerate very low control delays, which even ultra-low latency 5G edge computing can sometimes fail to achieve. In this work, we investigate the problem of cloud-based whole-body control of legged robots over a 5G link. We propose a novel approach that consists of a standard optimization-based controller on the network edge and a local linear, approximately optimal controller that significantly reduces on-board computational needs while increasing robustness to delay and possible loss of communication. Simulation experiments on humanoid balancing and walking tasks that includes a realistic 5G communication model demonstrate significant improvement of the reliability of robot locomotion under jitter and delays likely to be experienced in 5G wireless links.

We study the HRP-2Kai humanoid robot's ability to conduct the precise industrial task of fastening bolts in aircraft production. Our contribution stands mainly in high integration of different modules within the whole-body Quadratic Programing-based controller that has not been yet confronted to tasks demanding high precision in the execution and tuning. This includes the use of a robust visual servoing algorithm which allows the robot to move autonomously to a desired target and the design of specific tasks and estimators: a learning and admittance control that enables the robot to interact smoothly with its environment, and a fast and safe method to autonomously detect correct tool on nut insertion. We then show that the controller indeed enables our humanoid robot to achieve such a high precision task.

This paper describes a feasibility study for the use of robotics in offshore oil and gas exploration and production facilities. One application for robotics with potential to reduce operational expenditure and health, safety and environment exposure is a Mobile Universal Service Robot. The implementation of a first demonstrator and its evaluation in a real world field test is described afterwards. Assisted tele-operation as well as the capability to manipulate process equipment were two requests raised by offshore professionals during this field test. Finally this paper describes approaches of the ImRoNet project to achieve these requests.

This paper analyzes the potential to apply mobile service robots in offshore oil and gas producing environments. The required hardware and software components and abilities of such a mobile offshore inspection and manipulation robot are presented in this paper. Possible applications of mobile service robots in an offshore environment range from simple visual inspection tasks to physical intervention with the process equipment, e.g. for sample taking, valve turning, cleaning up minor obstructions, and operating control panels. The first prototype of a mobile offshore inspection robot is equipped with a robotic arm which carries a camera for visual inspection as well as various application sensors such as a microphone, gas and fire sensors. It is able of both, remote and autonomous inspection of industrial process equipment. In automatic mode the robot autonomously executes pre-programmed inspection tasks. The results of all inspection tasks are saved to a database and can be reviewed by the responsible operator in the central control room at any time. The evaluation of the first autonomous mobile robot that has ever been operated in offshore environments has proven the applicability of mobile robotics to offshore environments. Different types of inspection tasks (visual and acoustic inspection, gas measuring) have been programmed to and executed by the robot successfully without ever jeopardizing the safety of the platform or the platform personnel. The application of mobile robotics in offshore environments can reduce the level of manual human intervention required to operate a production facility thereby increasing the efficiency of the workforce, improving safety and working conditions, and improving the production economics. The successful evaluation of the first realization of a mobile inspection and manipulation robot has thus leveled the ground for future mobile robot installations in offshore environments.