Towards S-NAMO: Socially-aware Navigation Among Movable Obstacles

International audienc

PREDICTIVE POTENTIAL FIELD-BASED COLLISION AVOIDANCE FOR MULTICOPTERS

Reliable obstacle avoidance is a key to navigating with UAVs in the close vicinity of static and dynamic obstacles. Wheel-based mobile robots are often equipped with 2D or 3D laser range finders that cover the 2D workspace sufficiently accurate and at a high rate. Micro UAV platforms operate in a 3D environment, but the restricted payload prohibits the use of fast state-of-the-art 3D sensors. Thus, perception of small obstacles is often only possible in the vicinity of the UAV and a fast collision avoidance system is necessary. We propose a reactive collision avoidance system based on artificial potential fields, that takes the special dynamics of UAVs into account by predicting the influence of obstacles on the estimated trajectory in the near future using a learned motion model. Experimental evaluation shows that the prediction leads to smoother trajectories and allows to navigate collision-free through passageways

TOWARDS MULTIMODAL OMNIDIRECTIONAL OBSTACLE DETECTION FOR AUTONOMOUS UNMANNED AERIAL VEHICLES

Limiting factors for increasing autonomy and complexity of truly autonomous systems (without external sensing and control) are onboard sensing and onboard processing power. In this paper, we propose a hardware setup and processing pipeline that allows a fully autonomous UAV to perceive obstacles in (almost) all directions in its surroundings. Different sensor modalities are applied in order take into account the different characteristics of obstacles that can commonly be found in typical UAV applications. We provide a complete overview on the implemented system and present experimental results as a proof of concept

Overmars, Automatic construction of high quality roadmaps for path planning

Path planning plays an important role in many virtual worlds, like computer games. Currently the motion of entities is often planned using a combination of scripting, grid-search methods, local reactive methods, flocking and crowd behavior. In this paper we describe a new approach, based on a technique from robotics, that computes a roadmap of smooth, collision-free, high-quality paths. This roadmap can be used to obtain instantly good paths for entities. We also describe applications of the technique for planning the motion of groups of entities and for creating smooth camera movements through an environment.

Experimental evaluation of a joint cognitive system for 4D trajectory management

Effective joint human-automation coordination is essential in order to support the central role of the human operator in foreseen future trajectory-based air traffic operations. The SESAR WP-E project C-SHARE aims to achieve this by taking a Cognitive Systems Engineering approach, based upon accomplishing joint human and automation cognition through a shared representation of 4D-trajectory management. In foregoing research, a work domain model and a joint human-machine interface has been developed to support the human operator in the task of en-route 4D trajectory re-planning. This paper presents the findings of two experiments that aimed to determine the effect of both the initial level of traffic orderliness (i.e., structured versus unstructured traffic) and the scale of perturbations acting upon the airspace (e.g., number of conflicts and restricted areas) on the overall effectiveness of such a system. The findings of the experimental evaluation show that the C-SHARE approach to joint human-automation coordination in perturbation management is promising. Further, the experiment subjects accepted the tool and found it supportive for the task at hand, resulting in a manageable degree of workload during all experiment scenarios

Experimental evaluation of a joint cognitive system for 4D trajectory management

Effective joint human-automation coordination is essential in order to support the central role of the human operator in foreseen future trajectory-based air traffic operations. The SESAR WP-E project C-SHARE aims to achieve this by taking a Cognitive Systems Engineering approach, based upon accomplishing joint human and automation cognition through a shared representation of 4D-trajectory management. In foregoing research, a work domain model and a joint human-machine interface has been developed to support the human operator in the task of en-route 4D trajectory re-planning. This paper presents the findings of two experiments that aimed to determine the effect of both the initial level of traffic orderliness (i.e., structured versus unstructured traffic) and the scale of perturbations acting upon the airspace (e.g., number of conflicts and restricted areas) on the overall effectiveness of such a system. The findings of the experimental evaluation show that the C-SHARE approach to joint human-automation coordination in perturbation management is promising. Further, the experiment subjects accepted the tool and found it supportive for the task at hand, resulting in a manageable degree of workload during all experiment scenarios