Finding a needle in an exponential haystack: Discrete RRT for exploration of implicit roadmaps in multi-robot motion planning

We present a sampling-based framework for multi-robot motion planning which combines an implicit representation of a roadmap with a novel approach for pathfinding in geometrically embedded graphs tailored for our setting. Our pathfinding algorithm, discrete-RRT (dRRT), is an adaptation of the celebrated RRT algorithm for the discrete case of a graph, and it enables a rapid exploration of the high-dimensional configuration space by carefully walking through an implicit representation of a tensor product of roadmaps for the individual robots. We demonstrate our approach experimentally on scenarios of up to 60 degrees of freedom where our algorithm is faster by a factor of at least ten when compared to existing algorithms that we are aware of.Comment: Kiril Solovey and Oren Salzman contributed equally to this pape

The minimum energy expenditure shortest path method

This article discusses the addition of an energy parameter to the shortest path execution process; namely, the energy expenditure by a character during execution of the path. Given a simple environment in which a character has the ability to perform actions related to locomotion, such as walking and stair stepping, current techniques execute the shortest path based on the length of the extracted root trajectory. However, actual humans acting in constrained environments do not plan only according to shortest path criterion, they conceptually measure the path that minimizes the amount of energy expenditure. On this basis, it seems that virtual characters should also execute their paths according to the minimization of actual energy expenditure as well. In this article, a simple method that uses a formula for computing vanadium dioxide ($VO_2$) levels, which is a proxy for the energy expenditure by humans during various activities, is presented. The presented solution could be beneficial in any situation requiring a sophisticated perspective of the path-execution process. Moreover, it can be implemented in almost every path-planning method that has the ability to measure stepping actions or other actions of a virtual character

Collided path replanning in dynamic environments using RRT and Cell decomposition algorithms

The motion planning is an important part of robots’ models. It is responsible for robot’s movements. In this work, the cell decomposition algorithm is used to find a spatial path on preliminary static workspaces, and then, the rapidly exploring random tree algorithm (RRT) is used to validate this path on the actual workspace. Two methods have been proposed to enhance the omnidirectional robot’s navigation on partially changed workspace. First, the planner creates a RRT tree and biases its growth toward the path’s points in ordered form. The planner reduces the probability of choosing the next point when a collision is detected, which in turn increases the RRT’s expansion on the free space. The second method uses a straight planner to connect path’s points. If a collision is detected, the planner places RRTs on both sides of the collided segment. The proposed methods are compared with the others approaches, and the simulation shows better results in term of efficiency and completeness.Plánování pohybu robota je důležitou součástí modelování funkcí robotů. Plán řídí pohyby robota. V této práci se algoritmus rozkladu na buňky používá k nalezení cesty pracovní plochou a algoritmus prozkoumání náhodného stromu (RRT) k ověření cesty skutečným prostorem. Byly navrženy dvě metody ke zlepšení navigace všesměrové pohyblivého robota částečně změněnou pracovní plochou. Za prvé, plánovač vytvoří RRT strom a vychyluje jeho růst směrem k bodu na cestě. Plánovač snižuje pravděpodobnost výběru dalšího bodu, když je detekována kolize, což zase zvyšuje expanzi RRT na volném prostoru. Druhá metoda používá shodný plánovač pro napojení bodů cesty. Pokud je detekována kolize, plánovač upravuje RRT na obou stranách kolizního segmentu. Navrhované metody jsou porovnávány s dalšími používanými přístupy, přečemž simulace ukazuje lepší výsledky z hlediska účinnosti a úplnosti plánování cesty.The motion planning is an important part of robots’ models. It is responsible for robot’s movements. In this work, the cell decomposition algorithm is used to find a spatial path on preliminary static workspaces, and then, the rapidly exploring random tree algorithm (RRT) is used to validate this path on the actual workspace. Two methods have been proposed to enhance the omnidirectional robot’s navigation on partially changed workspace. First, the planner creates a RRT tree and biases its growth toward the path’s points in ordered form. The planner reduces the probability of choosing the next point when a collision is detected, which in turn increases the RRT’s expansion on the free space. The second method uses a straight planner to connect path’s points. If a collision is detected, the planner places RRTs on both sides of the collided segment. The proposed methods are compared with the others approaches, and the simulation shows better results in term of efficiency and completeness

A Hierarchical Hybrid Architecture for Mission-Oriented Robot Control

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-03413-3_26In this work is presented a general architecture for a multi physical agent network system based on the coordination and the behaviour management. The system is organised in a hierarchical structure where are distinguished the individual agent actions and the collective ones linked to the whole agent network. Individual actions are also organised in a hybrid layered system that take advantages from reactive and deliberative control. Sensing system is involved as well in the behaviour architecture improving the information acquisition performance.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under the CICYT project Mission Based Control (COBAMI): DPI2011-28507-C02-02, under coordinated project High Integrity Partitioned Embedded Systems (Hi-PartES): TIN2011-28567-C03-03, and under the collaborative research project supported by the European Union MultiPARTES Project: FP7-ICT 287702. 2011-14.Muñoz Alcobendas, M.; Munera Sánchez, E.; Blanes Noguera, F.; Simó Ten, JE. (2013). A Hierarchical Hybrid Architecture for Mission-Oriented Robot Control. En ROBOT2013: First Iberian Robotics Conference: Advances in Robotics, Vol. 1. Springer. 363-380. https://doi.org/10.1007/978-3-319-03413-3_26S363380Aragues, R.: Consistent data association in multi-robot systems with limited communications. 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Monocular Vision based Navigation in GPS-Denied Riverine Environments

This paper presents a new method to estimate the range and bearing of landmarks and solve the simultaneous localization and mapping (SLAM) problem. The proposed ranging and SLAM algorithms have application to a micro aerial vehicle (MAV) flying through riverine environments which occasionally involve heavy foliage and forest canopy. Monocular vision navigation has merits in MAV applications since it is lightweight and provides abundant visual cues of the environment in comparison to other ranging methods. In this paper, we suggest a monocular vision strategy incorporating image segmentation and epipolar geometry to extend the capability of the ranging method to unknown outdoor environments. The validity of our proposed method is verified through experiments in a river-like environment

Efficient exploration of unknown indoor environments using a team of mobile robots

Whenever multiple robots have to solve a common task, they need to coordinate their actions to carry out the task efficiently and to avoid interferences between individual robots. This is especially the case when considering the problem of exploring an unknown environment with a team of mobile robots. To achieve efficient terrain coverage with the sensors of the robots, one first needs to identify unknown areas in the environment. Second, one has to assign target locations to the individual robots so that they gather new and relevant information about the environment with their sensors. This assignment should lead to a distribution of the robots over the environment in a way that they avoid redundant work and do not interfere with each other by, for example, blocking their paths. In this paper, we address the problem of efficiently coordinating a large team of mobile robots. To better distribute the robots over the environment and to avoid redundant work, we take into account the type of place a potential target is located in (e.g., a corridor or a room). This knowledge allows us to improve the distribution of robots over the environment compared to approaches lacking this capability. To autonomously determine the type of a place, we apply a classifier learned using the AdaBoost algorithm. The resulting classifier takes laser range data as input and is able to classify the current location with high accuracy. We additionally use a hidden Markov model to consider the spatial dependencies between nearby locations. Our approach to incorporate the information about the type of places in the assignment process has been implemented and tested in different environments. The experiments illustrate that our system effectively distributes the robots over the environment and allows them to accomplish their mission faster compared to approaches that ignore the place labels

Combating COVID-19-The role of robotics in managing public health and infectious diseases.

COVID-19 may drive sustained research in robotics to address risks of infectious diseases

MVCSLAM: Mono-Vision Corner SLAM for Autonomous Micro-Helicopters in GPS Denied Environments

We present a real-time vision navigation and ranging method (VINAR) for the purpose of Simultaneous Localization and Mapping (SLAM) using monocular vision. Our navigation strategy assumes a GPS denied unknown environment, whose indoor architecture is represented via corner based feature points obtained through a monocular camera. We experiment on a case study mission of vision based SLAM through a conventional maze of corridors in a large building with an autonomous Micro Aerial Vehicle (MAV). We propose a method for gathering useful landmarks from a monocular camera for SLAM use. We make use of the corners by exploiting the architectural features of the manmade indoors