https://dialnet.unirioja.es/descarga/articulo/4511388.pdf

Rules of encounter: designing conventions for automated negotiation among computers

The Robot Operating System: Package reuse and community dynamics

The goal of coverage path planning is to create a path that covers the entire free space in a given environment. Coverage path planning is the most important component of cleaning robot technology, because it determines the cleaning robot’s movement. When the environment covered by a cleaning robot is extremely large and contains many obstacles, the computation for coverage path planning can be complicated. This can result in significant degradation of the execution time for coverage path planning. Not many studies have focused on the scalability of coverage path planning methods. In this paper, we propose a scalable coverage path planning method based on rectangular map decomposition. The experimental results demonstrate that the proposed method reduces the execution time for coverage path planning up to 14 times when compared with conventional methods.

Scalable Coverage Path Planning for Cleaning Robots Using Rectangular Map Decomposition on Large Environments

Uncertainty in motion planning is often caused by three main sources: motion error, sensing error, and imperfect environment map. Despite the significant effect of all three sources of uncertainty to motion planning problems, most planners take into account only one or at most two of them. We propose a new motion planner, called Guided Cluster Sampling (GCS), that takes into account all three sources of uncertainty for robots with active sensing capabilities. GCS uses the Partially Observable Markov Decision Process (POMDP) framework and the point-based POMDP approach. Although point-based POMDPs have shown impressive progress over the past few years, it performs poorly when the environment map is imperfect. This poor performance is due to the extremely high dimensional state space, which translates to the extremely large belief space B. We alleviate this problem by constructing a more suitable sampling distribution based on the observations that when the robot has active sensing capability, B can be partitioned into a collection of much smaller sub-spaces, and an optimal policy can often be generated by sufficient sampling of a small subset of the collection. Utilizing these observations, GCS samples B in two-stages, a subspace is sampled from the collection and then a belief is sampled from the subspace. It uses information from the set of sampled sub-spaces and sampled beliefs to guide subsequent sampling. Simulation results on marine robotics scenarios suggest that GCS can generate reasonable policies for motion planning problems with uncertain motion, sensing, and environment map, that are unsolvable by the best point-based POMDPs today. Furthermore, GCS handles POMDPs with continuous state, action, and observation spaces. We show that for a class of POMDPs that often occur in robot motion planning, given enough time, GCS converges to the optimal policy. To the best of our knowledge, this is the first convergence result for point-based POMDPs with continuous action space.

Global Motion Planning under Uncertain Motion, Sensing, and Environment Map

https://hal.archives-ouvertes.fr/hal-01005516/document

Telemedicine using mobile telecommunication: Towards syntactic interoperability in teleexpertise

- This paper focuses on the experimental analysts of Contract NET protocol for Multi-Robot task allocation. The problem domain consists of multiple vacuum cleaning robots that need to cooperate for cleaning an area that is beyond the capabilities of a single robot. A robot simulator has been used to experiment with various area and robot locations, and the summary of the effort required to process the tasks has been recorded. Experimental results show that using Contract NET protocol alone is not sufficient to achieve optimal results in task allocation. A more advanced strategy with or without involving the Contract NET protocol is required. Possible strategies are outlined and their analysis is the subject of the future work.

Experimental analysis of contract net protocol in multi-robot task allocation

A multi-agent system, comprising a server and at least two robots controllably wirelessly connected with the server, where the server contains at least one software agent allocated to each robot, which software agent provides abstraction from the robot being implemented in the hardware and enables to calculate, analyze, optimize and control the implementation of different scenarios of robots operation. The robots with marker sensors, odometers and collision sensors send their location and a (part of a) global map of a to a server with a global map, robots' positions and marker positions, the server updates the global map, plans work of the robots in optimal way, divides the environment in areas allocated to the robots.

Multi-robot system

This demonstration paper presents a prototype multi-robot system of vacuum cleaning robots. System was designed with the aim to join multiple robots in a team able to accomplish tasks that are beyond the capabilities of a single robot.

Multi-Robot System for Vacuum Cleaning Domain

Multi-Agent Robotic System Architecture for Effective Task Allocation and Management

Swarm robotics is a relatively new research area inspired from biological systems such as ant or bee colonies. It composes a system consisting of many small robots with simple control mechanisms capable of achieving complex collective behaviours on the swarm level such as aggregation, pattern formation and collective transportation to name a few. However, more research is still required to apply swarm robotics in practice. Within the scope of our knowledge at the moment there are no swarm robotics applications for real-life problems. The current research tends to solve specific tasks in controlled laboratory environments. In this paper we survey the existing works on swarm robotics and their applications and also analyse the potential of their applicability to solve real-life problems The analysis of the results of these studies shows that robot swarms are capable to solve these tasks satisfactory in controlled laboratory environments, at the same time there is no evidence of applying swarm robotics to solve real-life problems. The purpose of this paper is to take a step closer to bridging the gap between research in swarm robotics and their practical applications. We analyze the existing approaches in the field of swarm robotics and discuss their result applicability for solving real-life problems by outlining tasks that have been studied in the context of swarm robotics systems and analysing their potential practical applications. We also discuss how the tasks could be combined to achieve desirable practical results. Tasks of the swarm The potential applications of swarm robotics range from surveillance operations (15) to mine disarming in hostile environments (16). We believe it is essential to identify the tasks that can be solved using swarm robotics. According to recent literature reviews (1; 2; 17-19), swarm robotics has been studied in the context of the following tasks: Aggregation deals with spatially grouping all robots together in a region of the environment. Aggregation is used to get robots in a swarm sufficiently close together and can be used as a starting point for performing some additional tasks, such as communication with limited range. Aggregation near points of interest can be viewed as the first step of more complex tasks, such as collective

/pdf/towards-practical-application-of-swarm-robotics-overview-of-4eokcjuyp8.pdf

Aleksis Liekna

Papers

Experimental analysis of contract net protocol in multi-robot task allocation

Multi-robot system

Multi-Robot System for Vacuum Cleaning Domain

Multi-Agent Robotic System Architecture for Effective Task Allocation and Management

Towards practical application of swarm robotics: overview of swarm tasks