A Distributed Foraging Algorithm Based on Artificial Potential Field

International audienceSimple collections of agents that perform collectively and use distributed control algorithms constitute the interests of swarm robotics. A key issue to improve system performances is to effectively coordinate the team of agents. We present in this paper a multi-agent foraging algorithm called Cooperative-Color Marking Foraging Agents (C-CMFA). It uses the coordination rules of the S-MASA (Stigmergic Multi-Ant Search Area) algorithm to (i) speed up the search process and (ii) allow agents to build an optimal Artificial Potential Field (APF) simultaneously while exploring. To benefit from multiple robots, we add one cooperation rule in the algorithm to attract large number of agents to the found food. This algorithm constitutes a distributed and synchronous version of the c-marking algorithm. Simulation results in comparison with the c-marking one show the superiority of C-CMFA in different environment configurations

Autonomous construction using scarce resources in unknown environments - Ingredients for an intelligent robotic interaction with the physical world

The goal of creating machines that autonomously perform useful work in a safe, robust and intelligent manner continues to motivate robotics research. Achieving this autonomy requires capabilities for understanding the environment, physically interacting with it, predicting the outcomes of actions and reasoning with this knowledge. Such intelligent physical interaction was at the centre of early robotic investigations and remains an open topic. In this paper, we build on the fruit of decades of research to explore further this question in the context of autonomous construction in unknown environments with scarce resources. Our scenario involves a miniature mobile robot that autonomously maps an environment and uses cubes to bridge ditches and build vertical structures according to high-level goals given by a human. Based on a "real but contrived" experimental design, our results encompass practical insights for future applications that also need to integrate complex behaviours under hardware constraints, and shed light on the broader question of the capabilities required for intelligent physical interaction with the real world

Design and testing of a position adaptation system for KUKA robots using photoelectric sensors

This thesis presents the development and analysis of a position monitoring and adaptation system to be used in conjunction with a KUKA KR16-2 articulated robot using components readily available in most manufacturing settings. This system could be beneficial in the manufacturing sector in areas such as polymer welding and spray painting. In the former it could be used to maintain an effective distance between a welding end effector laying molten plastic and the surface area of the parts being welded, or in the case of the latter the system would be useful in painting objects of unknown shape or objects with unknown variations in the surface level. In the case of spray painting if you spray to close to an object you will get an inconsistent amount of paint applied to an area. This system would maintain the programmed distance between the robot system and target object. Typically, systems that achieve this level of control rely on expensive sensors such as force torque sensors. This research proposes to take the first step in trying to address the technical problems by introducing a novel way of adapting to a target surface deformation using comparably low cost photoelectric diffuse sensors. The key outcomes of this thesis can be found in the form of a software package to interface the photo-electric sensors to the KUKA robot system. This system is operated by a custom-built algorithm which is capable of dynamically calculating robot movements based off the sensor input. Additionally, an optimum system setup is developed with different configurations of sensor mounting and speeds of robot operation discussed and tested. The viability of the photo-electric diffuses sensors used in this application is also considered with further works suggested. Finally, a secondary application is developed for recording and analysing KUKA robot movements for use in other research activities

A distributed feedback mechanism to regulate wall construction by a robotic swarm

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Design and Performance Analysis of Genetic Algorithms for Topology Control Problems

In this dissertation, we present a bio-inspired decentralized topology control mechanism, called force-based genetic algorithm (FGA), where a genetic algorithm (GA) is run by each autonomous mobile node to achieve a uniform spread of mobile nodes and to provide a fully connected network over an unknown area. We present a formal analysis of FGA in terms of convergence speed, uniformity at area coverage, and Lyapunov stability theorem. This dissertation emphasizes the use of mobile nodes to achieve a uniform distribution over an unknown terrain without a priori information and a central control unit. In contrast, each mobile node running our FGA has to make its own movement direction and speed decisions based on local neighborhood information, such as obstacles and the number of neighbors, without a centralized control unit or global knowledge. We have implemented simulation software in Java and developed four different testbeds to study the effectiveness of different GA-based topology control frameworks for network performance metrics including node density, speed, and the number of generations that GAs run. The stochastic behavior of FGA, like all GA-based approaches, makes it difficult to analyze its convergence speed. We built metrically transitive homogeneous and inhomogeneous Markov chain models to analyze the convergence of our FGA with respect to the communication ranges of mobile nodes and the total number of nodes in the system. The Dobrushin contraction coefficient of ergodicity is used for measuring convergence speed for homogeneous and inhomogeneous Markov chain models of our FGA. Furthermore, convergence characteristic analysis helps us to choose the nearoptimal values for communication range, the number of mobile nodes, and the mean node degree before sending autonomous mobile nodes to any mission. Our analytical and experimental results show that our FGA delivers promising results for uniform mobile node distribution over unknown terrains. Since our FGA adapts to local environment rapidly and does not require global network knowledge, it can be used as a real-time topology controller for commercial and military applications