114 research outputs found
A macroscopic analytical model of collaboration in distributed robotic systems
In this article, we present a macroscopic analytical model of collaboration in a group of reactive robots. The model consists of a series of coupled differential equations that describe the dynamics of group behavior. After presenting the general model, we analyze in detail a case study of collaboration, the stick-pulling experiment, studied experimentally and in simulation by Ijspeert et al. [Autonomous Robots, 11, 149-171]. The robots' task is to pull sticks out of their holes, and it can be successfully achieved only through the collaboration of two robots. There is no explicit communication or coordination between the robots. Unlike microscopic simulations (sensor-based or using a probabilistic numerical model), in which computational time scales with the robot group size, the macroscopic model is computationally efficient, because its solutions are independent of robot group size. Analysis reproduces several qualitative conclusions of Ijspeert et al.: namely, the different dynamical regimes for different values of the ratio of robots to sticks, the existence of optimal control parameters that maximize system performance as a function of group size, and the transition from superlinear to sublinear performance as the number of robots is increased
Bounded Distributed Flocking Control of Nonholonomic Mobile Robots
There have been numerous studies on the problem of flocking control for
multiagent systems whose simplified models are presented in terms of point-mass
elements. Meanwhile, full dynamic models pose some challenging problems in
addressing the flocking control problem of mobile robots due to their
nonholonomic dynamic properties. Taking practical constraints into
consideration, we propose a novel approach to distributed flocking control of
nonholonomic mobile robots by bounded feedback. The flocking control objectives
consist of velocity consensus, collision avoidance, and cohesion maintenance
among mobile robots. A flocking control protocol which is based on the
information of neighbor mobile robots is constructed. The theoretical analysis
is conducted with the help of a Lyapunov-like function and graph theory.
Simulation results are shown to demonstrate the efficacy of the proposed
distributed flocking control scheme
MARBLER: An Open Platform for Standarized Evaluation of Multi-Robot Reinforcement Learning Algorithms
Multi-agent reinforcement learning (MARL) has enjoyed significant recent
progress, thanks to deep learning. This is naturally starting to benefit
multi-robot systems (MRS) in the form of multi-robot RL (MRRL). However,
existing infrastructure to train and evaluate policies predominantly focus on
challenges in coordinating virtual agents, and ignore characteristics important
to robotic systems. Few platforms support realistic robot dynamics, and fewer
still can evaluate Sim2Real performance of learned behavior. To address these
issues, we contribute MARBLER: Multi-Agent RL Benchmark and Learning
Environment for the Robotarium. MARBLER offers a robust and comprehensive
evaluation platform for MRRL by marrying Georgia Tech's Robotarium (which
enables rapid prototyping on physical MRS) and OpenAI's Gym framework (which
facilitates standardized use of modern learning algorithms). MARBLER offers a
highly controllable environment with realistic dynamics, including barrier
certificate-based obstacle avoidance. It allows anyone across the world to
train and deploy MRRL algorithms on a physical testbed with reproducibility.
Further, we introduce five novel scenarios inspired by common challenges in MRS
and provide support for new custom scenarios. Finally, we use MARBLER to
evaluate popular MARL algorithms and provide insights into their suitability
for MRRL. In summary, MARBLER can be a valuable tool to the MRS research
community by facilitating comprehensive and standardized evaluation of learning
algorithms on realistic simulations and physical hardware. Links to our
open-source framework and the videos of real-world experiments can be found at
https://shubhlohiya.github.io/MARBLER/.Comment: 7 pages, 3 figures, submitted to MRS 2023, for the associated
website, see https://shubhlohiya.github.io/MARBLER
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