In order to achieve autonomous operation of a vehicle in urban situations with unpredictable traffic, several realtime systems must interoperate, including environment perception, localization, planning, and control. In addition, a robust vehicle platform with appropriate sensors, computational hardware, networking, and software infrastructure is essential.

/pdf/towards-fully-autonomous-driving-systems-and-algorithms-58y9e7p2b1.pdf

Towards fully autonomous driving: Systems and algorithms

Intelligent vehicles have increased their capabilities for highly and, even fully, automated driving under controlled environments. Scene information is received using onboard sensors and communication network systems, i.e., infrastructure and other vehicles. Considering the available information, different motion planning and control techniques have been implemented to autonomously driving on complex environments. The main goal is focused on executing strategies to improve safety, comfort, and energy optimization. However, research challenges such as navigation in urban dynamic environments with obstacle avoidance capabilities, i.e., vulnerable road users (VRU) and vehicles, and cooperative maneuvers among automated and semi-automated vehicles still need further efforts for a real environment implementation. This paper presents a review of motion planning techniques implemented in the intelligent vehicles literature. A description of the technique used by research teams, their contributions in motion planning, and a comparison among these techniques is also presented. Relevant works in the overtaking and obstacle avoidance maneuvers are presented, allowing the understanding of the gaps and challenges to be addressed in the next years. Finally, an overview of future research direction and applications is given.

A Review of Motion Planning Techniques for Automated Vehicles

Environment models serve as important resources for an autonomous robot by providing it with the necessary task-relevant information about its habitat. Their use enables robots to perform their tasks more reliably, flexibly, and efficiently. As autonomous robotic platforms get more sophisticated manipulation capabilities, they also need more expressive and comprehensive environment models: for manipulation purposes their models have to include the objects present in the world, together with their position, form, and other aspects, as well as an interpretation of these objects with respect to the robot tasks. The dissertation presented in this article (Rusu, PhD thesis, 2009) proposes Semantic 3D Object Models as a novel representation of the robot’s operating environment that satisfies these requirements and shows how these models can be automatically acquired from dense 3D range data.

/pdf/semantic-3d-object-maps-for-everyday-manipulation-in-human-van9xaq8u7.pdf

Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments

We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar, or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally, for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.

/pdf/data-driven-grasp-synthesis-a-survey-3ahemc6ci5.pdf

Data-Driven Grasp Synthesis—A Survey

125 years after Bertha Benz completed the first overland journey in automotive history, the Mercedes Benz S-Class S 500 INTELLIGENT DRIVE followed the same route from Mannheim to Pforzheim, Germany, in fully autonomous manner. The autonomous vehicle was equipped with close-to-production sensor hardware and relied solely on vision and radar sensors in combination with accurate digital maps to obtain a comprehensive understanding of complex traffic situations. The historic Bertha Benz Memorial Route is particularly challenging for autonomous driving. The course taken by the autonomous vehicle had a length of 103 km and covered rural roads, 23 small villages and major cities (e.g. downtown Mannheim and Heidelberg). The route posed a large variety of difficult traffic scenarios including intersections with and without traffic lights, roundabouts, and narrow passages with oncoming traffic. This paper gives an overview of the autonomous vehicle and presents details on vision and radar-based perception, digital road maps and video-based self-localization, as well as motion planning in complex urban scenarios.

/pdf/making-bertha-drive-an-autonomous-journey-on-a-historic-2bwti38gqt.pdf

Making Bertha Drive?An Autonomous Journey on a Historic Route

In this paper, we present a new humanoid robot currently being developed for applications in human-centered environments In order for humanoid robots to enter human-centered environments, it is indispensable to equip them with manipulative, perceptive and communicative skills necessary for real-time interaction with the environment and humans The goal of our work is to provide reliable and highly integrated humanoid platforms which on the one hand allow the implementation and tests of various research activities and on the other hand the realization of service tasks in a household scenario We introduce the different subsystems of the robot We present the kinematics, sensors, and the hardware and software architecture We propose a hierarchically organized architecture and introduce the mapping of the functional features in this architecture into hardware and software modules We also describe different skills related to real-time object localization and motor control, which have been realized and integrated into the entire control architecture

/pdf/armar-iii-an-integrated-humanoid-platform-for-sensory-motor-2txpwtctal.pdf

ARMAR-III: An Integrated Humanoid Platform for Sensory-Motor Control

This paper reports on AnnieWAY, an autonomous vehicle that is capable of driving through urban scenarios and that successfully entered the finals of the 2007 DARPA Urban Challenge competition. After describing the main challenges imposed and the major hardware components, we outline the underlying software structure and focus on selected algorithms. Environmental perception mainly relies on a recent laser scanner that delivers both range and reflectivity measurements. Whereas range measurements are used to provide three-dimensional scene geometry, measuring reflectivity allows for robust lane marker detection. Mission and maneuver planning is conducted using a hierarchical state machine that generates behavior in accordance with California traffic laws. We conclude with a report of the results achieved during the competition. © 2008 Wiley Periodicals, Inc.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/rob.20252

Team AnnieWAY's autonomous system for the 2007 DARPA Urban Challenge

https://his.anthropomatik.kit.edu/pdf_humanoids/Asfour2008b.pdf

Toward humanoid manipulation in human-centred environments

Building a model of the environment is essential for mobile robotics. It allows the robot to reason about its sourroundings and plan actions according to its intentions. To enable safe motion planning it is vital to anticipate object movements. This paper presents an improved formulation for occupancy filtering. Our approach is closely related to the Bayesian Occupancy Filter (BOF) presented in [4]. The basic idea of occupancy filters is to represent the environment as a 2-dimensional grid of cells holding information about their state of occupancy and velocity. To improve the accuracy of predictions, prior knowledge about the motion preferences is used, derived from map data that can be obtained from navigation systems. In combination with a physically accurate transition model, it is possible to estimate the environment dynamics. Experiments show that this yields reliable estimates even for occluded regions.

Bayesian Occupancy grid Filter for dynamic environments using prior map knowledge

We propose a method for navigating a car-like vehicle within an unstructured environment. Path planning is posed as a graph search problem. The search graph is set up in a way that implies derivation of a feed forward term for a downstream closed loop controller. An informed search algorithm is used that is guided by a heuristic cost function that accounts for both kinematic constraints of the vehicle and the topology of the vehiclepsilas free space. Configuration space obstacles are computed from an obstacle map acquired from a high definition laser range scanner and search is restricted to the collision free subset of the configuration space. The algorithm allows for solving all of the following problems: Precise parking maneuvers, narrow turns, long distance navigation. The system has been used successfully on board the autonomous car ANNIEWAY in the DARPA urban challenge competition of 2007.

/pdf/navigating-car-like-robots-in-unstructured-environments-9xafdmyyzw.pdf

Joachim Schröder

Papers

ARMAR-III: An Integrated Humanoid Platform for Sensory-Motor Control

Team AnnieWAY's autonomous system for the 2007 DARPA Urban Challenge

Toward humanoid manipulation in human-centred environments

Bayesian Occupancy grid Filter for dynamic environments using prior map knowledge

Navigating car-like robots in unstructured environments using an obstacle sensitive cost function