Humanoid navigation and heavy load transportation in a cluttered environment

International audienceAlthough in recent years several studies aimed at the navigation of robots in cluttered environments, just a few have addressed the problem of robots navigating while moving a large or heavy object. This is especially useful when transporting loads with variable weights and shapes without having to change the robot hardware. On one hand, a major advantage of using a humanoid robot to move an object is that it has arms to firmly grasp it and control it. On the other hand, humanoid robots tend to have higher drift than their wheeled counterparts as well as having significant lateral swing while walking, which propagates to anything they carry. In this work, we present algorithms for a humanoid robot navigating in a cluttered environment while pushing a cart-like object. In addition, the algorithms make use of the hands and arms to articulate the cart when executing tight turns using whole body control scheme to reduce the lateral swing effect on the load and ensure a safe transport. Experiments conducted on a real Nao robot assessed the proposed approach and algorithms, they show that the payload of a humanoid robot can be significantly increased without changing the humanoid robot's hardware, and therefore enact the capacity of humanoid robots in real-life situations

Route planning methods for a modular warehouse system

In this study, procedures are presented that can be used to determine the routes of the packages transported within a modular storage system. The problem is a variant of robot motion planning problem. The structures of the procedures are developed in three steps for the simultaneous movement of multiple unit-sized packages in a modular warehouse. The proposed heuristic methods consist of route planning, tagging, and main control components. In order to demonstrate the solution performance of the methods, various experiments were conducted with different data sets and the solution times and qualities of the proposed methods were compared with previous studies. It was found that the proposed methods provide better solutions when taking the number of steps and solution time into consideration

Self-organizing robot formations using velocity potential fields commands for material transfer

Mobile robot formations differ in accordance with the mission, environment, and robot abilities. In the case of decentralized control, the ability to achieve the shapes of these formations needs to be built in the controllers of each autonomous robot. In this paper, self-organizing formations control for material transfer is investigated, as an alternative to automatic guided vehicles. Leader–follower approach is applied for controllers design to drive the robots toward the goal. The results confirm the ability of velocity potential approach for motion control of both self-organizing formations

Application of the LIDAR technology for obstacle detection during the operation of agricultural vehicles

. 2013. Application of the LIDAR technology for obstacle detection during the operation of agricultural vehicles. Canadian Biosystems Engineering/Le génie des biosystèmes au Canada 55: 2.9-2.16. Many algorithms have been proposed in the literature for the detection of foreign objects or obstacles to the operation of autonomous vehicles. However, a comparative evaluation of these existing approaches is still lacking. In this study, multiple feature recognition algorithms (average height, density, connectivity, and discontinuity methods) were evaluated for the identification of three types of foreign objects placed in four types of crops (range of crop height: 20 -80 cm) under different field and operating conditions. The field experiments were completed using a SICK laser measurement system (LMS) 291-S14 scanner that was placed on a tractor to scan standing crops in which the standard test objects had been placed. The data collected by the sensor was analyzed using the software MATLAB 2D and 3D versions. The average height method allowed for a 72.4% average object detection rate while the connectivity method only resulted in a successful object detection rate of 18% for all the experiments. It was also found that the crop density or foliage cover had a negative impact on the detection rate for shorter test objects with the higher rates of obstacle detection being achieved for objects significantly taller than crops. Increasing vehicle speed was also found to reduce detection abilities due to lower scan resolution per distance travelled. Keywords: Laser Measurement System, object detection, crops, foreign objects, autonomous vehicles, safety. Plusieurs algorithmes ont été développés pour déterminer la position d'objets pouvant entraver le fonctionnement de véhicules autonomes. Il reste cependant à compléter une évaluation comparative de ces différentes approches. Dans le cadre de cette étude, l'efficacité de quatre algorithmes de reconnaissance et de détection d'obstacles (méthodes basées sur la hauteur moyenne, la densité, la connectivité et la discontinuité) pour la détection de trois types d'obstacles placés dans quatre types de récolte différentes (gamme de hauteur des plantes : 20 -80 cm) a été comparée pour différentes combinaisons de conditions d'opération au champ. Les tests ont été complétés à l'aide d'un capteur SICK de mesure au laser (LMS) 291-S14 installé sur un tracteur agricole afin de détecter des objets placés à des endroits prédéterminés à proximité de la trajectoire du tracteur. Les données recueillies par le capteur ont été analysées à l'aide du logiciel MATLAB (versions 2D et 3D). La méthode de la hauteur moyenne a permis d'atteindre un taux de détection global des obstacles au champ de 72,4% alors que la méthode de la connectivité a fourni les résultats les moins intéressants avec un taux de détection global de seulement 18%. Les résultats obtenus indiquent également que la taille des cultures ainsi que la densité du couvert végétal ont résulté en des taux de détection des obstacles moins élevés. Les taux de détection ont également été moins élevés dans le cas des obstacles de petite taille alors que les taux de détection des objets dont la hauteur dépasse celle des cultures étaient plus élevés. L'accroissement de la vitesse d'avancement du tracteur a eu un impact négatif sur le taux de détection des obstacles. Mots-clés: système de détection laser, détection d'obstacles, cultures, véhicule autonome, sécurité

Cooperative SLAM for multiple UGVs navigation using SVSF filter

The aim of this paper is to present a cooperative simultaneous localization and mapping (CSLAM) solution based on a laser telemeter. The proposed solution gives the opportunity to a group of unmanned ground vehicles (UGVs) to construct a large map and localize themselves without any human intervention. Many solutions proposed to solve this problem, most of them are based on the sequential probabilistic approach, based around Extended Kalman Filter (EKF) or the Rao-Blackwellized particle filter. In our work, we propose a new alternative to avoid these limitations, a novel alternative solution based on the smooth variable structure filter (SVSF) to solve the UGV SLAM problem is proposed. This version of SVSF-SLAM algorithm uses a boundary layer width vector and does not require covariance derivation. The new algorithm has been developed to implement the SVSF filter for CSLAM. Our contribution deals with adapting the SVSF to solve the CSLAM problem for multiple UGVs. The algorithms developed in this work were implemented using a swarm of mobile robots Pioneer 3–AT. Two mapping approaches, point-based and line-based, are implemented and validated experimentally using 2D laser telemeter sensors. Good results are obtained by the Cooperative SVSF-SLAM algorithm compared with the Cooperative EKF-SLAM

A Visual Analytics Approach to Debugging Cooperative, Autonomous Multi-Robot Systems' Worldviews

Autonomous multi-robot systems, where a team of robots shares information to perform tasks that are beyond an individual robot's abilities, hold great promise for a number of applications, such as planetary exploration missions. Each robot in a multi-robot system that uses the shared-world coordination paradigm autonomously schedules which robot should perform a given task, and when, using its worldview--the robot's internal representation of its belief about both its own state, and other robots' states. A key problem for operators is that robots' worldviews can fall out of sync (often due to weak communication links), leading to desynchronization of the robots' scheduling decisions and inconsistent emergent behavior (e.g., tasks not performed, or performed by multiple robots). Operators face the time-consuming and difficult task of making sense of the robots' scheduling decisions, detecting de-synchronizations, and pinpointing the cause by comparing every robot's worldview. To address these challenges, we introduce MOSAIC Viewer, a visual analytics system that helps operators (i) make sense of the robots' schedules and (ii) detect and conduct a root cause analysis of the robots' desynchronized worldviews. Over a year-long partnership with roboticists at the NASA Jet Propulsion Laboratory, we conduct a formative study to identify the necessary system design requirements and a qualitative evaluation with 12 roboticists. We find that MOSAIC Viewer is faster- and easier-to-use than the users' current approaches, and it allows them to stitch low-level details to formulate a high-level understanding of the robots' schedules and detect and pinpoint the cause of the desynchronized worldviews.Comment: To appear in IEEE Conference on Visual Analytics Science and Technology (VAST) 202