Technical session talks from ICRA 2012
TechTalks from event: Technical session talks from ICRA 2012
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Multi-Robot Systems II
Distributed Value Functions for Multi-Robot ExplorationThis paper addresses the problem of exploring an unknown area with a team of autonomous robots using decentralized decision making techniques. The localization aspect is not considered and it is assumed the robots share their positions and have access to a map updated with all explored areas. A key problem is then the coordination of decentralized decision processes: each individual robot must choose appropriate exploration goals so that the team simultaneously explores different locations of the environment. We formalize this problem as a Decentralized Markov Decision Process (Dec-MDP) solved as a set of individual MDPs, where interactions between MDPs are considered in a distributed value function. Thus each robot computes locally a strategy that minimizes the interactions between the robots and maximizes the space coverage of the team. Our technique has been implemented and evaluated in real-world and simulated experiments.
Steiner Traveler: Relay Deployment for Remote Sensing in Heterogeneous Multi-Robot ExplorationIn the multi-robot exploration task of an unknown environment, human operators often need to control the robots remotely and obtain the sensed information by real-time bandwidth-consuming multimedia streams. The task has military and civilian applications, such as reconnaissance, search and rescue missions in earthquake, radioactive, and other dangerous or hostile regions. Due to the nature of such applications, infrastructure networks or pre-deployed relays are often not available to support the stream transmission. To address this issue, we present a novel exploration scheme called Bandwidth aware Exploration with a Steiner Traveler (BEST). BEST has a heterogeneous robot team with a fixed number of frontier nodes(FNs) to sense the area iteratively. In addition, a relay-deployment node (RDN) tracks the FNs movement and places relays when necessary to support the video/audio streams aggregation to the base station. Therefore, the main problem is to find a minimum path for the relay-deployment robot to travel and the positions to deploy necessary relays to support the stream aggregation in each movement iteration. This problem inherits characteristics of both the Steiner minimum tree and traveling salesman problems. We model the novel problem as the minimum velocity Flow constrained Steiner Traveler problem (FST). Extensive simulations show BEST improves exploration efficiency by 62% on average compared to the state-of-the-art homogeneous robot exploration strategies.
Minimal Persistence Control on Dynamic Directed Graphs for Multi-Robot FormationGiven a multi-robot system, in order to preserve its geometric shape in a formation, the minimal persistence control addresses questions: (1) what pairwise communication connections have to be prescribed to minimize communication channels, and (2) which orientations of communication links are to be placed between robots. In this paper, we propose a minimal persistence control problem on multi-robot systems with underlying graphs, being directed and dynamically switching. We develop distributed algorithms based on the rank of the rigidity matrix and the pebble game method. The feasibility of the proposed methods is validated by simulations on the robotic simulator Webots and experiments on e-puck robot platform.
Distributed Formation Control of Unicycle RobotsIn this paper, we consider the problem of distributed formation control for a group of unicycle robots. We propose a control algorithm that solves the formation control problem in that it ensures that robots create a desired timeâ€“ varying formation shape while the formation as a whole follows a prescribed trajectory. Moreover, we show that it is also possible to obtain coordination of robots in the formation, regardless of the trajectory tracking of the formation. We illustrate the behavior of a group of robots controlled by the formation control algorithm proposed in this paper in a simulation study.
Multi-Level Formation Roadmaps for Collision-Free Dynamic Shape Changes with Non-holonomic TeamsTeams of robots can utilize formations to accomplish a task, such as maximizing the observability of an environment while maintaining connectivity. In a cluttered space, however, it might be necessary to automatically change formation to avoid obstacles. This work proposes a path planning approach for non-holonomic robots, where a team dynamically switches formations to reach a goal without collisions. The method introduces a multi-level graph, which can be constructed offline. Each level corresponds to a different formation and edges between levels allow for formation transitions. All edges satisfy curvature bounds and clearance requirements from obstacles. During the online phase, the method returns a path for a virtual leader, as well as the points along the path where the team should switch formations. Individual agents can compute their controls using kinematic formation controllers that operate in curvilinear coordinates. The approach guarantees that it is feasible for the agents to follow the trajectory returned. Simulations show that the online cost of the approach is small. The method returns solutions that maximize the maintenance of a desired formation while allowing the team to rearrange its configuration in the presence of obstacles.
An Unscented Model Predictive Control Approach to the Formation Control of Nonholonomic Mobile RobotsFormation control of nonholonomic robots in dynamic unstructured environments is a challenging task yet to be met. This paper presents the unscented model predictive control (UMPC) approach to tackle the formation control of multiple nonholonomic robots in unstructured environments. In unscented predictive control, the uncertainty propagation in the nonholonomic nonlinear motion model is approximated using the unscented transform. The collision avoidance constraints have been introduced as the chance constraints to model predictive control. The UMPC approach enables us to find a closed form of the collision avoidance probabilistic constraints. The desired pose of each robot in the formation is introduced through the local objective function of UMPC of each robot. The simulation results indicate the effective and robust performance of UMPC in unstructured environment in the presence of action disturbance and communication signal noise.