Market-based coordination in dynamic environments based on the Hoplites framework

Abstract

Abstract-This work focuses on multi-robot coordination based on the Hoplites framework for solving the multi-robot task allocation (MRTA) problem. Three variations of increasing complexity for the MRTA problem, spatial task allocation based on distance, spatial task allocation based on time and distance, and persistent coverage have been studied in this work. The Fast Marching Method (FMM) has been used for robot path planning and providing estimates of the plans that robots bid on, in the context of the market. The use of this framework for solving the persistent coverage problem provides interesting insights by taking a high-level approach that is different from the commonly used solutions to this problem such as computing robot trajectories to keep the desired coverage level. A high fidelity simulation tool, Webots, along with the Robotic Operating System (ROS) have been utilized to provide our simulations with similar complexity to the real world tests. Results confirm that this pipeline is a very effective tool for our evaluations given that our simulations closely follow the results in reality. By modifying the replanning to prevent having costly or invalid plans by means of priority planning and turn taking, and basing the coordination on maximum plan length as opposed to time, we have been able to make improvements and adapt the Hoplites framework to our applications. The proposed approach is able to solve the spatial task allocation and persistent coverage problems in general. However, there exist some limitations. Particularly, in the case of persistent coverage, this method is suitable for applications where moderate spatial resolutions are sufficient such as patrolling.

Risk-Based Human-Aware Multi-Robot Coordination in Dynamic Environments Shared with Humans

“…In our previous work, a study of MRTA in dynamic and noisy environments [18] for spatial task allocation confirmed the effectiveness of our Hoplites-based method [19] as a first step towards deployment of MRTA methods in social environments. In [20], we proposed a humanaware coordination method for MRTA that accounted for the distance overhead of human-aware paths in the local balance function of the robots and allowed for instant collaboration in socially blocking situations.…”

“…1 and Fig. 2) for our experiments that enable simulations with similar environmental richness to the real world experiments, as shown in [18].…”

“…This process is ruled by a market-based approach and constitutes the active coordination. For details about the Hoplites-based MRTA refer to [18] and [20].…”

“…Moreover, reliable risk estimation is the basis for devising adaptive replannig strategies that can accommodate the high dynamics of social environments. Currently, re-planning is done when a task is accomplished or for verifying the validity of a stored plan when a robot is on its way towards a task (refer to [18] for detail).…”

Adaptive Risk-Based Replanning For Human-Aware Multi-Robot Task Allocation With Local Perception

“…This process is ruled by a market-based approach and constitutes the active coordination. For details about our implementation of an Hoplites-based MRTA refer to [18].…”