Multi-Robot Map-Merging-Free Connectivity-Based Positioning and Tethering in Unknown Environments

We consider a set of static towers out of communication range of each other, in an environment with no global coordinates. We address the problem of deploying mobile robots, initially not necessarily within range of each other or of the static towers, to serve as gateways to connect the towers. Our robot positioning algorithm consists of a heuristically controlled exploration with sharing of labeled relative positioning information, without the need to merge maps. After connectivity is achieved, we further address the problem of the robots locating and tethering to an agent using only measurements of signal strengths, without any communication between the agent and the team of robots. We contribute a two-step tethering algorithm that uses a datadriven multi-robot RSSI-distance model. We illustrate our connectivity algorithm in simulation and compare the efficiency of different proposed heuristics. We demonstrate the efficacy of the most promising heuristic in a variety of realistic indoor scenarios. We finally present results of the successful performance of our tethering algorithm in simulation and with real robots in an actual office environment. Keywords: Multi-robot control; Multi-robot teamwork; Multi-robot WiFi-based communication

Representation, Planning, and Learning of Dynamic Ad Hoc Robot Teams

<p>Forming an effective multi-robot team to perform a task is a key problem in many domains. The performance of a multi-robot team depends on the robots the team is composed of, where each robot has different capabilities. Team performance has previously been modeled as the sum of single-robot capabilities, and these capabilities are assumed to be known.</p> <p>Is team performance just the sum of single-robot capabilities? This thesis is motivated by instances where agents perform differently depending on their teammates, i.e., there is synergy in the team. For example, in human sports teams, a well-trained team performs better than an allstars team composed of top players from around the world. This thesis introduces a novel model of team synergy — the Synergy Graph model — where the performance of a team depends on each robot’s individual capabilities and a task-based relationship among them.</p> <p>Robots are capable of learning to collaborate and improving team performance over time, and this thesis explores how such robots are represented in the Synergy Graph Model. This thesis contributes a novel algorithm that allocates training instances for the robots to improve, so as to form an effective multi-robot team.</p> <p>The goal of team formation is the optimal selection of a subset of robots to perform the task, and this thesis contributes team formation algorithms that use a Synergy Graph to form an effective multi-robot team with high performance. In particular, the performance of a team is modeled with a Normal distribution to represent the nondeterminism of the robots’ actions in a dynamic world, and this thesis introduces the concept of a δ-optimal team that trades off risk versus reward. Further, robots may fail from time to time, and this thesis considers the formation of a robust multi-robot team that attains high performance even if failures occur. This thesis considers ad hoc teams, where the robots of the team have not collaborated together, and so their capabilities and synergy are initially unknown.</p> <p>This thesis contributes a novel learning algorithm that uses observations of team performance to learn a Synergy Graph that models the capabilities and synergy of the team. Further, new robots may become available, and this thesis introduces an algorithm that iteratively updates a Synergy Graph with new robots.</p

Weighted Synergy Graphs for Role Assignment in Ad Hoc Heterogeneous Robot Teams

Abstract — Heterogeneous robot teams are formed to perform complex tasks that are sub-divided into different roles. In ad hoc domains, the capabilities of the robots and how well they perform as a team is initially unknown, and the goal is to find the optimal role assignment policy of the robots that will attain the highest value. In this paper, we formally define the weighted synergy graph for role assignment (WeSGRA), that models the capabilities of robots in different roles as Normal distributions, and uses a weighted graph structure to model how different role assignments affect the overall team value. We contribute a learning algorithm that learns a WeSGRA from training examples of role assignment policies and observed values, and a team formation algorithm that approximates the optimal role assignment policy. We evaluate our model and algorithms in extensive experiments, and show that the learning algorithm learns a WeSGRA model with high log-likelihood that is used to form a near-optimal team. Further, we apply the WeSGRA model to simulated robots in the RoboCup Rescue domain, and to real robots in a foraging task, and show that the role assignment policy found by WeSGRA attains a high value and outperforms other algorithms, thus demonstrating the efficacy of the WeSGRA model. I

Modeling Mutual Capabilities in Heterogeneous Teams for Role Assignment

Abstract — The performance of a heterogeneous team depends critically on the composition of its members, and switching out one member for another can make a drastic difference. The capabilities of an agent depends not only on its individual characteristics, but also the interactions with its teammates. Roles are typically assigned to individual agents in such a team, where each role is responsible for a certain aspect of the joint team goal. In this paper, we focus on role assignment in a heterogeneous team, where an agent’s capability depends on its teammate and their mutual state, i.e., the agent’s state and its teammate’s state. The capabilities of an agent are represented by a mean and variance, to capture the uncertainty in the agent’s actions and in the world. We present a formal framework for representing this problem, and illustrate our framework using a robot soccer example. We formally describe how to compute the value of a role assignment policy, as well as the computation of the optimal role assignment policy, using a notion of risk. Further, we show that finding the optimal role assignment can be difficult, and describe approximation algorithms that can be used to solve this problem. We provide an analysis of these algorithms in our model and empirically show that they perform well in general problems of this domain, compared to market-based techniques. Lastly, we describe an extension to our proposed model that captures mutual interactions between more than two agents. I