Modeling Swarm-Based, Distributed Robotic Manipulation
William Agassounon, Kjerstin Easton, and Alcherio Martinoli
Collaborators: Joel Burdick, Kristina Lerman, Wulfram Gerstner

Abstract. We developed a macroscopic modeling methodology for swarm-based, distributed robotic manipulation. The methodology is well-suited for nonspatial metrics as it does not take into account robots’ trajectories or the spatial distribution of objects in the environment. The strength of the proposed models is that they have been built up incrementally, with matching between models and embodied simulations (and sometimes, real robot experiments) verified at each step as new complexity was added. Precise heuristic criteria based on geometrical considerations and systematic tests with one or two embodied agents prevent the introduction of free parameters into the model. Two concrete case-studies were considered. The first case-study, referred to as the aggregation experiment, is a non-collaborative manipulation concerned with gathering and clustering small objects initially scattered in an enclosed arena. The other case-study is involves strictly collaborative manipulation and is referred to as the stick-pulling experiment, as the robots’ task is to collaborate to pull sticks out of holes in the arena floor. Results show that the proposed approach delivers quantitatively accurate predictions, in particular for nonspatial metrics related to both the aggregation and stick-pulling processes, and constitutes a computationally efficient tool. The simplicity of the modeling methodology suggests that it is easily applicable to other experiments characterized by different agent capabilities and individual control algorithms. (full report)

Distributed Exploration and Coverage
Nikolaus Correll, Kjerstin Easton, Alcherio Martinoli, and Joel Burdick
Collaborators: Jonathan Witt, Edmond Wong (NASA Glenn Center)

Abstract. The aim of this project is to formulate an efficient exploration and coverage algorithm for a swarm of mobile agents. We present a completely distributed algorithm relying on agents endowed with identical controllers. The controller for the individual agent is realized through a hybrid approach using deliberative planning together with reactive behavior for collision avoidance. To exchange information about task progress the agents exploit a cellular decomposition of the environment. Coverage is performed using a grid-based algorithm (the Spanning Tree Coverage algorithm). Interaction between the agents is constrained to decentralized line-of-sight communication with limited range. The algorithm has been proved regarding completeness and its performance has been systematically investigated using an embodied simulator. (full report)

Evolutionary Design Synthesis – From Sensors to Controllers
Yizhen Zhang, Alcherio Martinoli, Erik Antonsson
Collaborators: Jonathan Litt, Edmond Wong (NASA Glenn Center

Abstract. In this project, an automated engineering design synthesis methodology based on evolutionary methodology is being explored, with special interest on design and optimization of distributed embodied systems. Two case studies have been considered so far; the first one concerns the design of a collective sensory system for traffic monitoring purposes, while the second one deals with the development of neural-based robot controllers for turbine blades inspection. It has been shown that the evolutionary methodology is able to address the engineering design challenges present in the case studies as well as other complex design problems, and synthesize novel design solutions of good quality. Moreover, the fitness function can be formulated as an aggregation of fuzzy design preferences with different weights and trade-off strategies leading to an automatic generation of the complete Pareto-optimal frontier.. (full report)