Sen3Bot Net: a meta-sensors network to enable smart factories implementation

In the near future, an increasing number of mobile agents working closely with human operators is envisaged in smart factories. In industrial human-shared environments that employ traditional Automated Guided Vehicles, safety can be ensured thanks to the support provided by Autonomous Mobile Robots, acting as a net of meta-sensors. The localization and perception information of each meta-sensor is shared among all mobile platforms. In particular, the information about the dynamic detection of human presence is combined and uploaded in a shared map, increasing the awareness of the mobile robots about their surroundings in a specific working area. This paper proposes an architecture that integrates the meta-sensors with an existing net of Automated Guided Vehicles, with the aim of enhancing systems based on outdated mobile agents that seek for Industry 4.0 solutions without the necessity of a complete renewal. Simulations of test scenarios are provided in order to confirm the validity of the proposed architecture model

Robots learn to behave: improving human-robot collaboration in flexible manufacturing applications

L'abstract è presente nell'allegato / the abstract is in the attachmen

Online supervised global path planning for AMRs with human-obstacle avoidance

In smart factories, the performance of the production lines is improved thanks to the wide application of mobile robots. In workspaces where human operators and mobile robots coexist, safety is a fundamental factor to be considered. In this context, the motion planning of Autonomous Mobile Robots is a challenging task, since it must take into account the human factor. In this paper, an implementation of a three-level online path planning is proposed, in which a set of waypoints belonging to a safe path is computed by a supervisory planner. Depending on the nature of the detected obstacles during the robot motion, the re-computation of the safe path may be enabled, after the collision avoidance action provided by the local planner is initiated. Particular attention is devoted to the detection and avoidance of human operators. The supervisory planner is triggered as the detected human gets sufficiently close to the mobile robot, allowing it to follow a new safe virtual path while conservatively circumnavigating the operator. The proposed algorithm has been experimentally validated in a laboratory environment emulating industrial scenarios

Safe navigation and human-robot interaction in assistant robotic applications

L'abstract è presente nell'allegato / the abstract is in the attachmen

Data-driven framework to improve collaborative human-robot flexible manufacturing applications

The manufacturing assembly lines of the future are foreseen to dismiss fully unmanned systems in favour of anthropocentric solutions. However, bringing in the human complexity leads to modeling and control questions that only data can answer. Moreover, many human-robot collaborative applications in flexible manufacturing involve manipulator cobots, whereas little attention is given to the role of mobile robots. This work outlines a data-driven framework, which is the core of a brand new project to be fully developed in the very next future, to let human-robot collaborative processes overcome the barriers to successful interaction, leveraging mobile and fixed-base robots

Experience-driven optimal motion synthesis in complex and shared environments

Optimal loco-manipulation planning and control for high-dimensional systems based on general, non-linear optimisation allows for the specification of versatile motion subject to complex constraints. However, complex, non-linear system and environment dynamics, switching contacts, and collision avoidance in cluttered environments introduce non-convexity and discontinuity in the optimisation space. This renders finding optimal solutions in complex and changing environments an open and challenging problem in robotics. Global optimisation methods can take a prohibitively long time to converge. Slow convergence makes them unsuitable for live deployment and online re-planning of motion policies in response to changes in the task or environment. Local optimisation techniques, in contrast, converge fast within the basin of attraction of a minimum but may not converge at all without a good initial guess as they can easily get stuck in local minima. Local methods are, therefore, a suitable choice provided we can supply a good initial guess. If a similarity between problems can be found and exploited, a memory of optimal solutions can be computed and compressed efficiently in an offline computation process. During runtime, we can query this memory to bootstrap motion synthesis by providing a good initial seed to the local optimisation solver. In order to realise such a system, we need to address several connected problems and questions: First, the formulation of the optimisation problem (and its parametrisation to allow solutions to transfer to new scenarios), and related, the type and granularity of user input, along with a strategy for recovery and feedback in case of unexpected changes or failure. Second, a sampling strategy during the database/memory generation that explores the parameter space efficiently without resorting to exhaustive measures---i.e., to balance storage size/memory with online runtime to adapt/repair the initial guess. Third, the question of how to represent the problem and environment to parametrise, compute, store, retrieve, and exploit the memory efficiently during pre-computation and runtime. One strategy to make the problem computationally tractable is to decompose planning into a series of sequential sub-problems, e.g., contact-before-motion approaches which sequentially perform goal state planning, contact planning, motion planning, and encoding. Here, subsequent stages operate within the null-space of the constraints of the prior problem, such as the contact mode or sequence. This doctoral thesis follows this line of work. It investigates general optimisation-based formulations for motion synthesis along with a strategy for exploration, encoding, and exploitation of a versatile memory-of-motion for providing an initial guess to optimisation solvers. In particular, we focus on manipulation in complex environments with high-dimensional robot systems such as humanoids and mobile manipulators. The first part of this thesis focuses on collision-free motion generation to reliably generate motions. We present a general, collision-free inverse kinematics method using a combination of gradient-based local optimisation with random/evolution strategy restarting to achieve high success rates and avoid local minima. We use formulations for discrete collision avoidance and introduce a novel, computationally fast continuous collision avoidance objective based on conservative advancement and harmonic potential fields. Using this, we can synthesise continuous-time collision-free motion plans in the presence of moving obstacles. It further enables to discretise trajectories with fewer waypoints, which in turn considerably reduces the optimisation problem complexity, and thus, time to solve. The second part focuses on problem representations and exploration. We first introduce an efficient solution encoding for trajectory library-based approaches. This representation, paired with an accompanying exploration strategy for offline pre-computation, permits the application of inexpensive distance metrics during runtime. We demonstrate how our method efficiently re-uses trajectory samples, increases planning success rates, and reduces planning time while being highly memory-efficient. We subsequently present a method to explore the topological features of the solution space using tools from computational homology. This enables us to cluster solutions according to their inherent structure which increases the success of warm-starting for problems with discontinuities and multi-modality. The third part focuses on real-world deployment in laboratory and field experiments as well as incorporating user input. We present a framework for robust shared autonomy with a focus on continuous scene monitoring for assured safety. This framework further supports interactive adjustment of autonomy levels from fully teleoperated to automatic execution of stored behaviour sequences. Finally, we present sensing and control for the integration and embodiment of the presented methodology in high-dimensional real-world platforms used in laboratory experiments and real-world deployment. We validate our presented methods using hardware experiments on a variety of robot platforms demonstrating generalisation to other robots and environments

Qualitative Probabilistic Models of HRSI for Safe Situational Human-Aware Navigation

For adoption of Autonomous Mobile Robots (AMR) across a breadth of industries, they must navigate around humans in a way which is safe and which humans perceive as safe, but without greatly compromising efficiency. This work proposes a novel classifier of the Human-Robot Spatial Interaction (HRSI) situation of an interacting human and robot, to be applied in Human-Aware Navigation (HAN) to account for situational context. A classifier comprised of per-situation Hidden Markov Models is developed, and trained with sequences of states in Qualitative Trajectory Calculus, representing relative human and robot movements in various HRSI situations. This multi-HMM HRSI situation classifier is created as a component of the safety stack for the EU Horizon 2020 ILIAD Project, and the theoretical foundation and implementation of this system is described, along with the results of a HRI study that evaluates the classification performance of this work’s novel classifier. The aim of this work is to demonstrate accurate continuous real-time classification of a set of socially legible HRSI situations that occur when a proximate human and heavy industrial robot are moving through a shared space. High classification performance is demonstrated, with future work currently being conducted by ILIAD colleagues to test a complete HAN system that employs this real-time situation classification to apply situational qualitative motion constraints, as well as testing the ILIAD safety stack as a whole

Sensor data fusion for smart AMRs in human-shared industrial workspaces

A growing presence of mobile agents is envisaged in the smart factories scenario of the next future. The safe motion of traditional Automated Guided Vehicles in human-shared workspaces can be achieved thanks to the support of a fleet of Autonomous Mobile Robots, acting as a net of meta-sensors, able to detect the human presence and share the information. This paper proposes a preliminary working implementation of one meta-sensor module, exploiting the synergistic use of different sensors through an overall affordable and accessible sensor data fusion algorithm. Experimental results in a laboratory environment confirm the validity of the approach