Supervisory Control Theory (SCT) provides a formal framework for controlling discrete event systems. It has recently been used to generate correct-by-construction controllers for swarm robotics systems. Current SCT frameworks are limited, as they support only (private) events that are observable within the same robot. In this paper, we propose an extended SCT framework that incorporates (public) events that are shared among robots. The extended framework allows to model formally the interactions among the robots. It is evaluated using a case study, where a group of mobile robots need to synchronise their movements in space and time-a requirement that is specified at the formal level. We validate our approach through experiments with groups of e-puck robots.

Autonomous exploration in robotics is a crucial feature to achieve robust and safe systems capable to interact with and recognize their surrounding environment. In this paper, we present a method for object recognition using a three-fingered robotic hand actively exploring interesting object locations to reduce uncertainty. We present a novel probabilistic perception approach with a Bayesian formulation to iteratively accumulate evidence from robot touch. Exploration of better locations for perception is performed by familiarity and novelty exploration behaviors, which intelligently control the robot hand to move toward locations with low and high levels of interestingness, respectively. These are active behaviors that, similar to the exploratory procedures observed in humans, allow robots to autonomously explore locations they believe that contain interesting information for recognition. Active behaviors are validated with object recognition experiments in both offline and real-time modes. Furthermore, the effects of inhibiting the active behaviors are analyzed with a passive exploration strategy. The results from the experiments demonstrate the accuracy of our proposed methods, but also their benefits for active robot control to intelligently explore and interact with the environment.

Water is a highly valuable resource so asset management of associated infrastructure is of critical importance. Water distribution pipe networks are usually buried, and so are difficult to access. Robots are therefore appealing for performing inspection and detecting damage to target repairs. However, robot mapping and localisation of buried water pipes has not been widely investigated to date, and is challenging because pipes tend to be relatively featureless. In this paper we propose a mapping and localisation algorithm for metal water pipes with two key novelties: the development of a new type of map based on hydrophone induced vibration signals of metal pipes, and a mapping algorithm based on spatial warping and averaging of dead reckoning signals used to calibrate the map (using dynamic time warping). Localisation is performed using both terrain-based extended Kalman filtering and also particle filtering. We successfully demonstrate and evaluate the approach on a combination of experimental and simulation data, showing improved localisation compared to dead reckoning.

High value manufacturing (HVM) is a key sector; however, its products have a long development time. Adaptation in factories according to production requirements has been proposed to increase HVM's efficiency. In self-reconfigurable factories there is rapid relocation of production resources to perform different tasks. Due to this flexibility, the allocation and scheduling of jobs to resources is a key challenge that needs to be solved with tractable methods. A prior problem resides in the vast number of ways to formulate the allocation and scheduling problem (ASP). The ASP formulation might consider variables such as the number of resources, their locations and capacities; and their number increases by adding variables or options, e.g. a problem with 50 binary variables has order of ~1015. Due to this complexity, here is proposed a novel methodology to compare abstract formulations (optimization scenarios). Its novelty resides in the systematic and hierarchical generation of partial scenarios in sequential stages with partial groups of variables; then experts select partial scenarios at each stage to become the bases for more complex scenarios at successive stages. A case study is presented and addressed with the methodology, the results show a reduction of more than 90% generated scenarios.

Magnetic adhesion mechanism is commonly used in mobile robots design for inspection tasks of ferric structures. The magnetic adhesion force is generated either by Permanent Magnets (PMs) or Electro-Magnets (EMs). Both methods have intrinsic limitations: fixed magnetic force in PMs or a constant electric power supply in EMs. Work in this paper presents a novel active magnetic wheel design by implementing Electro- Permanent Magnet (EPM) technology in its adhesion mechanism. EPMs allow to control the magnetic force by simply applying a short electric pulse. Controlling the amplitude of the electrical pulse, the magnetic force can be set into a chosen value enabling a continuous variation of the magnetic adhesive force. Implementing EPMs in adhesion mechanism will impact significantly on the mobile robot when manoeuvring in complex structures, avoiding difficult obstacles and energy consumption, thus enhancing its performance. Simulations of the proposed wheel design are performed in order to demonstrate the behaviour of the active magnetic adhesion mechanism and its feasibility. An improved design of the actual wheel is presented based on results of initial simulations. Further designs are suggested by adapting the adhesion mechanism to existing wheel designs.

The practical effectiveness of modular robotic systems depends heavily on the connection mechanisms used to join their separate entities, particularly for those systems capable of self-reconfiguration. This work presents HiGen, a high-speed genderless mechanical connection mechanism for the docking of robotic modules. HiGen connectors can join with one another in a manner that allows either side to disconnect in the event of failure. During connection electrical contacts are mated, supporting the concurrent use of local and global communication protocols, as well as power sharing techniques. Rapid actuation of the mechanism allows connections to be made and broken at a speed that is, to our knowledge, an order of magnitude faster than existing mechanical genderless approaches that feature single-sided disconnect, benefiting the reconfiguration time of modular robots. The HiGen connector is intended for future work in modular robotics, but could also see use in other areas of robotics for tool and payload attachment.

In this work, we present an active tactile perception approach for contour following based on a probabilistic framework. Tactile data were collected using a biomimetic fingertip sensor. We propose a control architecture that implements a perception-action cycle for the exploratory procedure, which allows the fingertip to react to tactile contact whilst regulating the applied contact force. In addition' the fingertip is actively repositioned to an optimal position to ensure accurate perception. The method is trained off-line and then the testing performed on-line based on contour following around several different test shapes. We then implement object recognition based on the extracted shapes. Our active approach is compared with a passive approach, demonstrating that active perception is necessary for successful contour following and hence shape recognition.

In this work, we apply active Bayesian perception to angle and position discrimination and extend the method to perform actions in a sensorimotor task using a biomimetic fingertip. The first part of this study tests active perception off-line with a large dataset of edge orientations and positions, using a Monte Carlo validation to ascertain the classification accuracy. We observe a significant improvement over passive methods that lack a sensorimotor loop for actively repositioning the sensor. The second part of this study then applies these findings about active perception to an example sensorimotor task in real-time. Using an appropriate online sensorimotor control architecture, the robot made decisions about what to do next and where to move next, which was applied to a contour-following task around several objects. The successful outcome of this simple but illustrative task demonstrates that active perception can be of practical benefit for tactile robotics.

Due to the changing trend in manufacturing field from mass production to mass customization it is important to offer new products at low cost and fast time to market to keep competitive. This paper addresses the problem of manufacturing services with fast production time, for multiple products and capacity for low and high production volumes. To provide these services, a novel framework, INTREPID, is proposed based on the idea of Self-Reconfigurable Manufacturing Systems (S-RMS). A factory that facilitates manufacturing layout reconfiguration and mobile production resources that can self-reconfigure depending on the product is proposed. The full scope of this framework is to provide global services through the cloud and a public manufacturing network. This will allow production in any country without huge investment for entities like SMEs, startups and even entrepreneurships.

As the speed of industrial pick and place robots continues to increase, new trajectory planning strategies will need to be developed in order to optimise the dynamic characteristics of high speed motion. The use of elliptical pick and place cycles coupled with a Modified Sine cam motion profile is proposed and compared to the traditional rectangular cycle with trapezoidal velocity profile. The elliptical cycle exhibits very smooth, continuous motion curves. Asymmetric acceleration can optionally be specified to reduce peak joint torques and increase maximum pick and place speed.

A convergence acceleration operator (CAO) is described which enhances the search capability and the speed of convergence of the host multiobjective optimization algorithm. The operator acts directly in the objective space to suggest improvements to solutions obtained by a multiobjective evolutionary algorithm (MOEA). The suggested improved objective vectors are then mapped into the decision variable space and tested. This method improves upon prior work in a number of important respects, such as mapping technique and solution improvement. Further, the paper discusses implications for many-objective problems and studies the impact of the use of the CAO as the number of objectives increases. The CAO is incorporated with two leading MOEAs, the non-dominated sorting genetic algorithm and the strength Pareto evolutionary algorithm and tested. Results show that the hybridized algorithms consistently improve the speed of convergence of the original algorithm while maintaining the desired distribution of solutions. It is shown that the operator is a transferable component that can be hybridized with any MOEA.