Tony J. Dodd

Active Bayesian perception for angle and position discrimination with a biomimetic fingertip

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.

Convergence Acceleration Operator for Multiobjective Optimization

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.

Self-organized aggregation without computation

This paper presents a solution to the problem of self-organized aggregation of embodied robots that requires no arithmetic computation. The robots have no memory and are equipped with one binary sensor, which informs them whether or not there is another robot in their line of sight. It is proven that the sensor needs to have a sufficiently long range; otherwise aggregation cannot be guaranteed, irrespective of the controller used. The optimal controller is found by performing a grid search over the space of all possible controllers. With this controller, robots rotate on the spot when they perceive another robot, and move backwards along a circular trajectory otherwise. This controller is proven to always aggregate two simultaneously moving robots in finite time, an upper bound for which is provided. Simulations show that the controller also aggregates at least 1000 robots into a single cluster consistently. Moreover, in 30 experiments with 40 physical e-puck robots, 98.6% of the robots aggregated into one cluster. The results obtained have profound implications for the implementation of multi-robot systems at scales where conventional approaches to sensing and information processing are no longer applicable.

Why `GSA: a gravitational search algorithm' is not genuinely based on the law of gravity

This letter highlights a fundamental inconsistency in the formulation of the Gravitational search algorithm (GSA) (Rashedi et al., Inf Sci 2232–48, 2009). GSA is said to be based on the law of gravity, that is, candidate solutions attract each other in the search space based on their relative distances and ‘masses’ (qualities). We show that, contrary to what is claimed, GSA does not take the distances between solutions into account, and therefore cannot be considered to be based on the law of gravity.

Design and optimization of magnetic wheel for wall and ceiling climbing robot

Magnetic wall and ceiling climbing robots have been proposed in many industrial applications where robots must move over ferromagnetic material surfaces. The magnetic circuit design with magnetic attractive force calculation of permanent magnetic wheel plays an important role which significantly affects the system reliability, payload ability and power consumption of the robot. In this paper, a flexible wall and ceiling climbing robot with six permanent magnetic wheels is proposed to climb along the vertical wall and overhead ceiling of steel cargo containers as part of an illegal contraband inspection system. The permanent magnetic wheels are designed to apply to the wall and ceiling climbing robot, whilst finite element method is employed to estimate the permanent magnetic wheels with various wheel rims. The distributions of magnetic flux lines and magnetic attractive forces are compared on both plane and corner scenarios so that the robot can adaptively travel through the convex and concave surfaces of the cargo container. Optimisation of wheel rims is presented to achieve the equivalent magnetic adhesive forces along with the estimation of magnetic ring dimensions in the axial and radial directions. Finally, the practical issues correlated with the applications of the techniques are discussed and the conclusions are drawn with further improvement and prototyping.

Supervisory control theory applied to swarm robotics

Currently, the control software of swarm robotics systems is created by ad hoc development. This makes it hard to deploy these systems in real-world scenarios. In particular, it is difficult to maintain, analyse, or verify the systems. Formal methods can contribute to overcome these problems. However, they usually do not guarantee that the implementation matches the specification, because the system’s control code is typically generated manually. Also, there is cultural resistance to apply formal methods; they may be perceived as an additional step that does not add value to the final product. To address these problems, we propose supervisory control theory for the domain of swarm robotics. The advantages of supervisory control theory, and its associated tools, are a reduction in the amount of ad hoc development, the automatic generation of control code from modelled specifications, proofs of properties over generated control code, and the reusability of formally designed controllers between different robotic platforms. These advantages are demonstrated in four case studies using the e-puck and Kilobot robot platforms. Experiments with up to 600 physical robots are reported, which show that supervisory control theory can be used to formally develop state-of-the-art solutions to a range of problems in swarm robotics.

A Bernoulli principle gripper for handling of planar and 3D (food) products

Purpose – The purpose of this paper is the increase the flexibility of robots used for handling of 3D (food) objects handling by the development and evaluation of a novel 3D Bernoulli gripper.Design/methodology/approach – A new gripper technology have been designed and evaluated. A deformable surface have been used to enable individual product handling. The lift force generated and the force exerted on the product during gripping is measured using a material tester instrument. Various products are tested with the gripper. A experimental/theoretical approach is used to explain the results.Findings – A deformable surface can be used to generate a lift force using the Bernoulli principle on 3D objects. Using a small forming a significant increase in the lift force generated is recorded. Increasing the forming further was shown to have little or even negative effects. The forces exerted on the product during forming was measured to be sufficiently low to avoid product damage.Research limitations/implications ...

A NEW SOLUTION TO VOLTERRA SERIES ESTIMATION

Abstract Volterra series expansions represent an important model for the representation, analysis and synthesis of nonlinear dynamical systems. However, a significant problem with this approach to system identification is that the number of terms required to be estimated grows exponentially with the order of the expansion. In practice, therefore, the Volterra series is typically truncated to consist of, at most, second degree terms only. In this paper it is shown how the ideas of reproducing kernel Hilbert spaces (RKHS) can be applied to provide a practicable solution to the problem of estimating Volterra series. The approach is based on solving for the Volterra series in a linearised feature space (corresponding to the Volterra series) which leads to a more parsimonious estimation problem.

Evolving Aggregation Behaviors in Multi-Robot Systems with Binary Sensors

This paper investigates a non-traditional sensing trade-off in swarm robotics: one in which each robot has a relatively long sensing range, but processes a minimal amount of information. Aggregation is used as a case study, where randomly-placed robots are required to meet at a common location without using environmental cues. The binary sensor used only lets a robot know whether or not there is another robot in its direct line of sight. Simulation results with both a memoryless controller (reactive) and a controller with memory (recurrent) prove that this sensor is enough to achieve error-free aggregation, as long as a sufficient sensing range is provided. The recurrent controller gave better results in simulation, and a post-evaluation with it shows that it is able to aggregate at least 1000 robots into a single cluster consistently. Simulation results also show that, with the recurrent controller, false negative noise on the sensor can speed up the aggregation process. The system has been implemented on 20 physical e-puck robots, and systematic experiments have been performed with both controllers: on average, 86-89% of the robots aggregated into a single cluster within 10 minutes.

Simulation of a Soar-Based Autonomous Mission Management System for Unmanned Aircraft

State-of-the-art unmanned aerial vehicles are typically able to autonomously execute a preplanned mission. However, unmanned aerial vehicles usually fly in a very dynamic environment that requires dynamic changes to the flight plan; this mission management activity is usually tasked to human supervision. Through the use of a set of theoretical concepts that allow the description of a flight plan, a software system that autonomously accomplishes the mission management task for an unmanned aerial vehicle was developed. The system was implemented using a combination of Soar intelligent agents and traditional control techniques, and it is thoroughly described in the first part of the paper. An extensive testing campaign, based on the use of a realistic simulation environment, was performed on the system; the second part of this paper presents results obtained during this campaign. The system was demonstrated to be capable of automatically generating and executing an entire flight plan after being assigned a set of objectives. In conclusion, possible future developments are discussed, focusing in particular on prospective hardware implementation for the system.