Distributed Coverage: From Deterministic to Probabilistic Models

For the multi-robot coverage problem determin- istic deliberative as well as probabilistic approaches have been proposed. Whereas deterministic approaches usually provide provable completeness and promise good performance under perfect conditions, probabilistic approaches are more robust to sensor and actuator noise, but completion cannot be guaranteed and performance is sub-optimal in terms of time to completion. In reality, however, almost all deterministic algorithms for robot coordination can be considered probabilistic when considering the unpredictability of real world factors. This paper investigates experimentally and analytically how probabilistic and deterministic algorithms can be combined for maintaining the robustness of probabilistic approaches, and explicitly model the reliability of a robotic platform. Using realistic simulation and data from real robot experiments, we study system performance of a swarm-robotic inspection system at different levels of noise (wheel-slip). The prediction error of a purely deterministic model increases when the assumption of perfect sensors and actuators is violated, whereas a combination of probabilistic and deterministic models provides a better match with experimental data

Probabilistic and Distributed Control of a Large-Scale Swarm of Autonomous Agents

We present a novel method for guiding a large-scale swarm of autonomous agents into a desired formation shape in a distributed and scalable manner. Our Probabilistic Swarm Guidance using Inhomogeneous Markov Chains (PSG-IMC) algorithm adopts an Eulerian framework, where the physical space is partitioned into bins and the swarm's density distribution over each bin is controlled. Each agent determines its bin transition probabilities using a time-inhomogeneous Markov chain. These time-varying Markov matrices are constructed by each agent in real-time using the feedback from the current swarm distribution, which is estimated in a distributed manner. The PSG-IMC algorithm minimizes the expected cost of the transitions per time instant, required to achieve and maintain the desired formation shape, even when agents are added to or removed from the swarm. The algorithm scales well with a large number of agents and complex formation shapes, and can also be adapted for area exploration applications. We demonstrate the effectiveness of this proposed swarm guidance algorithm by using results of numerical simulations and hardware experiments with multiple quadrotors.Comment: Submitted to IEEE Transactions on Robotic

Feedback-Based Inhomogeneous Markov Chain Approach To Probabilistic Swarm Guidance

This paper presents a novel and generic distributed swarm guidance algorithm using inhomogeneous Markov chains that guarantees superior performance over existing homogeneous Markov chain based algorithms, when the feedback of the current swarm distribution is available. The probabilistic swarm guidance using inhomogeneous Markov chain (PSG–IMC) algorithm guarantees sharper and faster convergence to the desired formation or unknown target distribution, minimizes the number of transitions for achieving and maintaining the formation even if the swarm is damaged or agents are added/removed from the swarm, and ensures that the agents settle down after the swarm’s objective is achieved. This PSG–IMC algorithm relies on a novel technique for constructing Markov matrices for a given stationary distribution. This technique incorporates the feedback of the current swarm distribution, minimizes the coefficient of ergodicity and the resulting Markov matrix satisfies motion constraints. This approach is validated using Monte Carlo simulations of the PSG–IMC algorithm for pattern formation and goal searching application

Feedback-Based Inhomogeneous Markov Chain Approach To Probabilistic Swarm Guidance

This paper presents a novel and generic distributed swarm guidance algorithm using inhomogeneous Markov chains that guarantees superior performance over existing homogeneous Markov chain based algorithms, when the feedback of the current swarm distribution is available. The probabilistic swarm guidance using inhomogeneous Markov chain (PSG–IMC) algorithm guarantees sharper and faster convergence to the desired formation or unknown target distribution, minimizes the number of transitions for achieving and maintaining the formation even if the swarm is damaged or agents are added/removed from the swarm, and ensures that the agents settle down after the swarm’s objective is achieved. This PSG–IMC algorithm relies on a novel technique for constructing Markov matrices for a given stationary distribution. This technique incorporates the feedback of the current swarm distribution, minimizes the coefficient of ergodicity and the resulting Markov matrix satisfies motion constraints. This approach is validated using Monte Carlo simulations of the PSG–IMC algorithm for pattern formation and goal searching application

Robust Distributed Coverage using a Swarm of Miniature Robots

For the multi-robot coverage problem deterministic deliberative as well as probabilistic approaches have been proposed. Whereas deterministic approaches usually provide provable completeness and promise good performance under perfect conditions, probabilistic approaches are more robust to sensor and actuator noise, but completion cannot be guaranteed and performance is sub-optimal in terms of time to completion. In reality, however, almost all deterministic algorithms for robot coordination can be considered probabilistic when considering the unpredictability of real world factors. This paper investigates experimentally and analytically how probabilistic and deterministic algorithms can be combined for maintaining the robustness of probabilistic approaches, and explicitly model the reliability of a robotic platform. Using realistic simulation and data from real robot experiments, we study system performance of a swarm-robotic inspection system at different levels of noise (wheel-slip). The prediction error of a purely deterministic model increases when the assumption of perfect sensors and actuators is violated, whereas a combination of probabilistic and deterministic models provides a better match with experimental data