Local ant system for allocating robot swarms to time-constrained tasks

We propose a novel application of the Ant Colony Optimization algorithm to efficiently allocate a swarm of homogeneous robots to a set of tasks that need to be accomplished by specific deadlines. We exploit the local communication between robots to periodically evaluate the quality of the allocation solutions, and agents select independently among the high-quality alternatives. The evaluation is performed using pheromone trails to favor allocations which minimize the execution time of the tasks. Our approach is validated in both static and dynamic environments (i.e. the task availability changes over time) using different sets of physics-based simulations. (C) 2018 Elsevier B.V. All rights reserved

Collision-aware Task Assignment for Multi-Robot Systems

We propose a novel formulation of the collision-aware task assignment (CATA) problem and a decentralized auction-based algorithm to solve the problem with optimality bound. Using a collision cone, we predict potential collisions and introduce a binary decision variable into the local reward function for task bidding. We further improve CATA by implementing a receding collision horizon to address the stopping robot scenario, i.e. when robots are confined to their task location and become static obstacles to other moving robots. The auction-based algorithm encourages the robots to bid for tasks with collision mitigation considerations. We validate the improved task assignment solution with both simulation and experimental results, which show significant reduction of overlapping paths as well as deadlocks

Spatio-Temporal Patterns act as Computational Mechanisms governing Emergent behavior in Robotic Swarms

open access articleOur goal is to control a robotic swarm without removing its swarm-like nature. In other words, we aim to intrinsically control a robotic swarm emergent behavior. Past attempts at governing robotic swarms or their selfcoordinating emergent behavior, has proven ineffective, largely due to the swarm’s inherent randomness (making it difficult to predict) and utter simplicity (they lack a leader, any kind of centralized control, long-range communication, global knowledge, complex internal models and only operate on a couple of basic, reactive rules). The main problem is that emergent phenomena itself is not fully understood, despite being at the forefront of current research. Research into 1D and 2D Cellular Automata has uncovered a hidden computational layer which bridges the micromacro gap (i.e., how individual behaviors at the micro-level influence the global behaviors on the macro-level). We hypothesize that there also lie embedded computational mechanisms at the heart of a robotic swarm’s emergent behavior. To test this theory, we proceeded to simulate robotic swarms (represented as both particles and dynamic networks) and then designed local rules to induce various types of intelligent, emergent behaviors (as well as designing genetic algorithms to evolve robotic swarms with emergent behaviors). Finally, we analysed these robotic swarms and successfully confirmed our hypothesis; analyzing their developments and interactions over time revealed various forms of embedded spatiotemporal patterns which store, propagate and parallel process information across the swarm according to some internal, collision-based logic (solving the mystery of how simple robots are able to self-coordinate and allow global behaviors to emerge across the swarm)

Construction task allocation through the collective perception of a dynamic environment

Building structures is a remarkable collective process but its automation remains an open challenge. Robot swarms provide a promising solution to this challenge. However, collective construction involves a number of difficulties regarding efficient robots allocation to the different activities, particularly if the goal is to reach an optimal construction rate. In this paper, we study an abstract construction scenario, where a swarm of robots is engaged in a collective perception process to estimate the density of building blocks around a construction site. The goal of this perception process is to maintain a minimum density of blocks available to the robots for construction. To maintain this density, the allocation of robots to the foraging task needs to be adjusted such that enough blocks are retrieved. Our results show a robust collective perception that enables the swarm to maintain a minimum block density under different rates of construction and foraging. Our approach leads the system to stabilize around a state in which the robots allocation allows the swarm to maintain a tile density that is close to or above the target minimum.info:eu-repo/semantics/publishedDorigo, M. Stützle, T. Blesa, M. J. Blum, C. Hamann, H. Heinrich, M. K. & Strobel, V. (2020). Swarm Intelligence: 12th International Conference, ANTS 2020, Barcelona, Spain, October 26-28, 2020, Proceedings. Cham: Springer International Publishing AG

Evolutionary swarm robotics: a theoretical and methodological itinerary from individual neuro-controllers to collective behaviours

In the last decade, swarm robotics gathered much attention in the research community. By drawing inspiration from social insects and other self-organizing systems, it focuses on large robot groups featuring distributed control, adaptation, high robustness, and flexibility. Various reasons lay behind this interest in similar multi-robot systems. Above all, inspiration comes from the observation of social activities, which are based on concepts like division of labor, cooperation, and communication. If societies are organized in such a way in order to be more efficient, then robotic groups also could benefit from similar paradigms