Obstacle Avoidance and Proscriptive Bayesian Programming

Unexpected events and not modeled properties of the robot environment are some of the challenges presented by situated robotics research field. Collision avoidance is a basic security requirement and this paper proposes a probabilistic approach called Bayesian Programming, which aims to deal with the uncertainty, imprecision and incompleteness of the information handled to solve the obstacle avoidance problem. Some examples illustrate the process of embodying the programmer preliminary knowledge into a Bayesian program and experimental results of these examples implementation in an electrical vehicle are described and commented. A video illustration of the developed experiments can be found at http://www.inrialpes.fr/sharp/pub/laplac

Implementing human-acceptable navigational behavior and fuzzy controller for an autonomous robot

Robots are just starting to appear in peopled environments but in order to be accepted by humans, they should obey basic people’s social rules. In particular, they have to be able to move around without disturbing people. This means that they have to obey the social rules that manage the movement of people, for example following virtual lanes when moving through corridors, not crossing in front of moving people, etc. In this paper some of these aspects are explained, as well as the implementation of preliminaries works to implement the proposed solutions are described. So, a slight modification to the Lane-Curvature Method is presented to improve the behavior of a mobile robot when crossing people in a corridor. Other works needed to test this modifications in the robot Amelia of the Reliable Autonomous Systems Lab, as the implementation of a fuzzy controller, are also described in this pape

Simulating use cases for the UAH autonomous electric car

2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 27-30 Oct. 2019This paper presents the simulation use cases for the UAH Autonomous Electric Car, related with typical driving scenarios in urban environments, focusing on the use of hierarchical interpreted binary Petri nets in order to implement the decision making framework of an autonomous electric vehicle. First, we describe our proposal of autonomous system architecture, which is based on the open source Robot Operating System (ROS) framework that allows the fusion of multiple sensors and the real-time processing and communication of multiple processes in different embedded processors. Then, the paper focuses on the study of some of the most interesting driving scenarios such as: stop, pedestrian crossing, Adaptive Cruise Control (ACC) and overtaking, illustrating both the executive module that carries out each behaviour based on Petri nets and the trajectory and linear velocity that allows to quantify the accuracy and robustness of the architecture proposal for environment perception, navigation and planning on a university Campus.Ministerio de Economía y CompetitividadComunidad de Madri

Improved dynamic window approach by using Lyapunov stability criteria

This paper presents improvements over the Dynamic Window Approach (I-DWA), used for computing in real time autonomous robot navigation. A novel objective function that includes Lyapunov stability criteria is proposed. It allows to guarantee a global and asymptotic convergence to the goal avoiding collisions and resulting in a more simple and self-contained approach. Experimental results with simulated and real environments are presented to validate the capability of the proposed approach. Additionally, comparisons with the original DWA are given

Moving Obstacles' Motion Prediction for Autonomous Navigation

voir basilic : http://emotion.inrialpes.fr/bibemotion/2004/VLFL04a/ address: Kunming, ChinaVehicle navigation in dynamic environments is an important challenge, especially when the motion of the objects populating the environment is unknown. Traditional motion planning approaches are too slow to be applied in real-time to this domain, hence, new techniques are needed. Recently, iterative planning has emerged as a promising approach. Nevertheless, existing iterative methods do not provide a way to es- timate the future behavior of moving obstacles and use the resulting estimates in trajectory computa- tion. This paper presents an iterative planning ap- proach that addresses these two issues. It consists of two complementary methods: 1) a motion prediction method which learns typical behaviors of objects in a given environment. 2) an iterative motion planning technique based on the concept of Velocity Obsta- cles

Real-Time Obstacle Avoidance for Polygonal Robots with a Reduced Dynamic Window

In this paper we present an approach to obstacle avoidance and local path planning for polygonal robots. It decomposes the task into a model stage and a planning stage. The model stage accounts for robot shape and dynamics using a reduced dynamic window. The planning stage produces collision-free local paths with a velocity profile. We present an analytical solution to the distance to collision problem for polygonal robots, avoiding thus the use of look-up tables. The approach has been tested in simulation and on two non-holonomic rectangular robots where a cycle time of 10 Hz was reached under full CPU load. During a longterm experiment over 5 km travel distance, the method demonstrated its practicability

An Integrated Approach to Real-Time Mobile Robot Control in Partially Known Indoor Environments

In this paper we present a navigation method for mobile robots in partially known indoor environments based on integration of graph based search algorithms and dynamic window local obstacle avoidance method. With the attention on a dynamic environment three different graph based search algorithms for global geometrical path planning are considered and compared: A*, D* and focussed D* algorithm. The admissible robot trajectories are generated in the dynamic window local obstacle avoider module that ensures safe robot operation. A simple and efficient procedure to the selection of appropriate motion commands based upon alignment of acquired trajectories and global geometric path is proposed. The initial a priori knowledge is used about environment in the form of the occupancy grid map that is incrementally updated in runtime. The algorithms were verified both in simulation and experimentally on a Pioneer 2DX mobile robot using laser range sensor where a good correlation was proven