Internal agent states : experiments using the swarm leader concept

In recent years, an understanding of the operating principles and stability of natural swarms has proven to be a useful tool for the design and control of artificial robotic agents. Many robotic systems, whose design or control principals are inspired by behavioural aspects of real biological systems such as leader-follower relationship, have been developed. We introduced an algorithm which successfully enhances the navigation performance of a swarm of robots using the swarm leader concept. This paper presents some applications based on that work using the simulations and experimental implementation using a swarming behaviour test-bed at the University of Strathclyde. Experimental and simulation results match closely in a way that confirms the efficiency of the algorithm as well as its applicability

A Portable Active Binocular Robot Vision Architecture for Scene Exploration

We present a portable active binocular robot vision archi- tecture that integrates a number of visual behaviours. This vision archi- tecture inherits the abilities of vergence, localisation, recognition and si- multaneous identification of multiple target object instances. To demon- strate the portability of our vision architecture, we carry out qualitative and comparative analysis under two different hardware robotic settings, feature extraction techniques and viewpoints. Our portable active binoc- ular robot vision architecture achieved average recognition rates of 93.5% for fronto-parallel viewpoints and, 83% percentage for anthropomorphic viewpoints, respectively

Discrimination of social tactile gestures using biomimetic skin

The implementation of novel tactile sensors has yielded original mechanisms for human-robot interaction that support the interpretation of complex social scenarios. For instance, the recognition of social tactile gestures is an important requirement in the design of robot companions because it enables the android to engage with human drives. We are interested on implementing such a functionality upon the biomimetic skin of the iCub android

Towards a swarm robotic system for autonomous cereal harvesting

Swarm robotics is an emerging technology that has the potential to revolutionise precision agriculture by coordinating fleets of small autonomous vehicles to minimise soil damage, increase farming resolution, lower the cost of automation, and provide solutions that are intrinsically safer and more sustainable than large monolithic systems. Here, we propose a novel swarm robotic system for autonomous harvesting of cereal crops such as wheat and barley. In contrast to existing agricultural swarm robotic systems, we intend to use small autonomous versions of traditional agricultural vehicles, in an attempt to narrow the skills gap for future end-users

Towards modeling complex robot training tasks through system identification

Previous research has shown that sensor-motor tasks in mobile robotics applications can be modelled automatically, using NARMAX system identi�cation, where the sensory perception of the robot is mapped to the desired motor commands using non-linear polynomial functions, resulting in a tight coupling between sensing and acting | the robot responds directly to the sensor stimuli without having internal states or memory. However, competences such as for instance sequences of actions, where actions depend on each other, require memory and thus a representation of state. In these cases a simple direct link between sensory perception and the motor commands may not be enough to accomplish the desired tasks. The contribution to knowledge of this paper is to show how fundamental, simple NARMAX models of behaviour can be used in a bootstrapping process to generate complex behaviours that were so far beyond reach. We argue that as the complexity of the task increases, it is important to estimate the current state of the robot and integrate this information into the system identification process. To achieve this we propose a novel method which relates distinctive locations in the environment to the state of the robot, using an unsupervised clustering algorithm. Once we estimate the current state of the robot accurately, we combine the state information with the perception of the robot through a bootstrapping method to generate more complex robot tasks: We obtain a polynomial model which models the complex task as a function of predefined low level sensor motor controllers and raw sensory data. The proposed method has been used to teach Scitos G5 mobile robots a number of complex tasks, such as advanced obstacle avoidance, or complex route learning

Reasoning on Grasp-Action Affordances

Artificial intelligence is essential to succeed in challenging activities that involve dynamic environments, such as object manipulation tasks in indoor scenes. Most of the state-of-the-art literature explores robotic grasping methods by focusing exclusively on attributes of the target object. When it comes to human perceptual learning approaches, these physical qualities are not only inferred from the object, but also from the characteristics of the surroundings. This work proposes a method that includes environmental context to reason on an object affordance to then deduce its grasping regions. This affordance is reasoned using a ranked association of visual semantic attributes harvested in a knowledge base graph representation. The framework is assessed using standard learning evaluation metrics and the zero-shot affordance prediction scenario. The resulting grasping areas are compared with unseen labelled data to asses their accuracy matching percentage. The outcome of this evaluation suggest the autonomy capabilities of the proposed method for object interaction applications in indoor environments.Comment: Annual Conference Towards Autonomous Robotic Systems (TAROS19

Children's age influences their use of biological and mechanical questions towards a humanoid

Complex autonomous interactions, biomimetic appearances, and responsive behaviours are increasingly seen in social robots. These features, by design or otherwise, may substantially influence young children’s beliefs of a robot’s animacy. Young children are believed to hold naive theories of animacy, and can miscategorise objects as living agents with intentions; however, this develops with age to a biological understanding. Prior research indicates that children frequently categorise a responsive humanoid as being a hybrid of person and machine; although, with age, children tend towards classifying the humanoid as being more machine-like. Our current research explores this phenomenon, using an unobtrusive method: recording childrens conversational interaction with the humanoid and classifying indications of animacy beliefs in childrens questions asked. Our results indicate that established findings are not an artefact of prior research methods: young children tend to converse with the humanoid as if it is more animate than older children do

Establishing Continuous Communication through Dynamic Team Behaviour Switching

Maintaining continuous communication is an important factor that contributes to the success of multi-robot systems. Most research involving multi-robot teams is conducted in controlled laboratory settings, where continuous communication is assumed, typically because there is a wireless network (wifi) that keeps all the robots connected. But for multi-robot teams to operate successfully “in the wild”, it is crucial to consider how communication can be maintained when signals fail or robots move out of range. This paper presents a novel “leader-follower behaviour” with dynamic role switching and messaging that supports uninterrupted communication, regardless of network perturbations. A series of experiments were conducted in which it is shown how network perturbations effect performance, comparing a baseline with the new leaderfollower behaviour. The experiments record metrics on team success, given the two conditions. These results are significant for real-world multi-robot systems applications that require continuous communication amongst team members

Toward robust visual odometry using prior 2D map information and multiple hypothesis particle filtering

Visual odometry can be used to estimate the pose of a robot from current and recent video frames. A problem with these methods is that they drift over time due to the accumulation of estimation errors at each time-step. In this short paper we propose and briefly demonstrate the potential benefit of using prior 2D, top-down map information combined with multiple hypothesis particle filtering to correct visual odometry estimates. The results demonstrate a substantial improvement in robustness and accuracy over the sole use of visual odometry