Towards a swarm robotic approach for cooperative object recognition

Social insects have inspired the behaviours of swarm robotic systems for the last 20 years. Interactions of the simple individuals in these swarms form solutions to relatively complex problems. A novel swarm robotic method is investigated for future robotic cooperative object recognition tasks. Previous multi-agent systems involve cameras and image analyses to identify objects. They cooperate only to improve their hypotheses of the shape's identity. The system proposed uses agents whose interactions with each other around the physical boundaries of the object's shape allow the distinguishing features found. The agents are a physical embodiment of the vision system, making them suitable for environments where it would not be possible to use a camera. A Simplified Hexagonal Model was developed to simulate and examine the strategies. The hexagonal cells of which can be empty, contain an agent (hBot) or part of an object shape. Initially the hBots are required to identify the valid object shapes from a set of two types of known shapes. To do this the hBots change state when in contact with an object and when touching other hBots of the same state level, where some states are only achieved when neighbouring certain object shapes. The agents are oblivious, anonymous and homogeneous. They also do not know their position or orientation and cannot distinguish between object shapes alone due to their limited sensor range. Further work increased the number of object shapes to provide a range of scenarios

Artificial Intelligence and Systems Theory: Applied to Cooperative Robots

This paper describes an approach to the design of a population of cooperative robots based on concepts borrowed from Systems Theory and Artificial Intelligence. The research has been developed under the SocRob project, carried out by the Intelligent Systems Laboratory at the Institute for Systems and Robotics - Instituto Superior Tecnico (ISR/IST) in Lisbon. The acronym of the project stands both for "Society of Robots" and "Soccer Robots", the case study where we are testing our population of robots. Designing soccer robots is a very challenging problem, where the robots must act not only to shoot a ball towards the goal, but also to detect and avoid static (walls, stopped robots) and dynamic (moving robots) obstacles. Furthermore, they must cooperate to defeat an opposing team. Our past and current research in soccer robotics includes cooperative sensor fusion for world modeling, object recognition and tracking, robot navigation, multi-robot distributed task planning and coordination, including cooperative reinforcement learning in cooperative and adversarial environments, and behavior-based architectures for real time task execution of cooperating robot teams

On Binary Max-Sum and Tractable HOPs

The Max-Sum message-passing algorithm has been used to approximately solve several unconstrained optimization problems, specially in the distributed context. In general, the complexity of computing messages is exponential. However, if the problem is modeled using the so called Tractable HOPs (THOPs), binary MaxSum's messages can be computed in polynomial time. In this paper we review existing THOPs, and present new ones, aiming at providing an updated view of efficient message computation.Peer Reviewe

A Framework for Cooperative Object Recognition

This paper explores the problem of object recognition from multiple observers. The basic idea is to overcome the limitations of the recognition module by integrating information from multiple sources. Each observer is capable of performing appearance-based object recognition, and through knowledge of their relative positions and orientations, the observerrs can coordinate their hypotheses to make object recognition more robust. A framework is proposed for appearance-based object recognition using Canny edgemapsthatare e ectively normalized tobetranslation and scale invariant. Object matching is formulated as a non-parametric statistical similarity computation between two distribution functions, while information integration is performed in a Bayesian belief net framework. Such nets enable bothacontinuous and a cooperative consideration of recognition result. Experiments which are reported on two observers recognizing mobile robots show a signi cant improvent of the recognition results.