596 research outputs found
Supervised Autonomous Locomotion and Manipulation for Disaster Response with a Centaur-like Robot
Mobile manipulation tasks are one of the key challenges in the field of
search and rescue (SAR) robotics requiring robots with flexible locomotion and
manipulation abilities. Since the tasks are mostly unknown in advance, the
robot has to adapt to a wide variety of terrains and workspaces during a
mission. The centaur-like robot Centauro has a hybrid legged-wheeled base and
an anthropomorphic upper body to carry out complex tasks in environments too
dangerous for humans. Due to its high number of degrees of freedom, controlling
the robot with direct teleoperation approaches is challenging and exhausting.
Supervised autonomy approaches are promising to increase quality and speed of
control while keeping the flexibility to solve unknown tasks. We developed a
set of operator assistance functionalities with different levels of autonomy to
control the robot for challenging locomotion and manipulation tasks. The
integrated system was evaluated in disaster response scenarios and showed
promising performance.Comment: In Proceedings of IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS), Madrid, Spain, October 201
Legged locomotion over irregular terrains: State of the art of human and robot performance
Legged robotic technologies have moved out of the lab to operate in real environments, characterized by a wide variety of unpredictable irregularities and disturbances, all this in close proximity with humans. Demonstrating the ability of current robots to move robustly and reliably in these conditions is becoming essential to prove their safe operation. Here, we report an in-depth literature review aimed at verifying the existence of common or agreed protocols and metrics to test the performance of legged system in realistic environments. We primarily focused on three types of robotic technologies, i.e., hexapods, quadrupeds and bipeds. We also included a comprehensive overview on human locomotion studies, being it often considered the gold standard for performance, and one of the most important sources of bioinspiration for legged machines. We discovered that very few papers have rigorously studied robotic locomotion under irregular terrain conditions. On the contrary, numerous studies have addressed this problem on human gait, being nonetheless of highly heterogeneous nature in terms of experimental design. This lack of agreed methodology makes it challenging for the community to properly assess, compare and predict the performance of existing legged systems in real environments. On the one hand, this work provides a library of methods, metrics and experimental protocols, with a critical analysis on the limitations of the current approaches and future promising directions. On the other hand, it demonstrates the existence of an important lack of benchmarks in the literature, and the possibility of bridging different disciplines, e.g., the human and robotic, towards the definition of standardized procedure that will boost not only the scientific development of better bioinspired solutions, but also their market uptake
Learning Ground Traversability from Simulations
Mobile ground robots operating on unstructured terrain must predict which
areas of the environment they are able to pass in order to plan feasible paths.
We address traversability estimation as a heightmap classification problem: we
build a convolutional neural network that, given an image representing the
heightmap of a terrain patch, predicts whether the robot will be able to
traverse such patch from left to right. The classifier is trained for a
specific robot model (wheeled, tracked, legged, snake-like) using simulation
data on procedurally generated training terrains; the trained classifier can be
applied to unseen large heightmaps to yield oriented traversability maps, and
then plan traversable paths. We extensively evaluate the approach in simulation
on six real-world elevation datasets, and run a real-robot validation in one
indoor and one outdoor environment.Comment: Webpage: http://romarcg.xyz/traversability_estimation
Quadrupedal Footstep Planning using Learned Motion Models of a Black-Box Controller
Legged robots are increasingly entering new domains and applications,
including search and rescue, inspection, and logistics. However, for such
systems to be valuable in real-world scenarios, they must be able to
autonomously and robustly navigate irregular terrains. In many cases, robots
that are sold on the market do not provide such abilities, being able to
perform only blind locomotion. Furthermore, their controller cannot be easily
modified by the end-user, requiring a new and time-consuming control synthesis.
In this work, we present a fast local motion planning pipeline that extends the
capabilities of a black-box walking controller that is only able to track
high-level reference velocities. More precisely, we learn a set of motion
models for such a controller that maps high-level velocity commands to Center
of Mass (CoM) and footstep motions. We then integrate these models with a
variant of the A star algorithm to plan the CoM trajectory, footstep sequences,
and corresponding high-level velocity commands based on visual information,
allowing the quadruped to safely traverse irregular terrains at demand
SafeSteps: Learning Safer Footstep Planning Policies for Legged Robots via Model-Based Priors
We present a footstep planning policy for quadrupedal locomotion that is able
to directly take into consideration a-priori safety information in its
decisions. At its core, a learning process analyzes terrain patches,
classifying each landing location by its kinematic feasibility, shin collision,
and terrain roughness. This information is then encoded into a small vector
representation and passed as an additional state to the footstep planning
policy, which furthermore proposes only safe footstep location by applying a
masked variant of the Proximal Policy Optimization (PPO) algorithm. The
performance of the proposed approach is shown by comparative simulations on an
electric quadruped robot walking in different rough terrain scenarios. We show
that violations of the above safety conditions are greatly reduced both during
training and the successive deployment of the policy, resulting in an
inherently safer footstep planner. Furthermore, we show how, as a byproduct,
fewer reward terms are needed to shape the behavior of the policy, which in
return is able to achieve both better final performances and sample efficienc
An Overview of Legged Robots
The objective of this paper is to present the evolution and the state-of-theart in the area of legged locomotion systems. In a first phase different possibilities for mobile robots are discussed, namely the case of artificial legged locomotion systems, while emphasizing their advantages and limitations. In a second phase an historical overview of the evolution of these systems is presented, bearing in mind several particular cases often considered as milestones on the technological and scientific progress. After this historical timeline, some of the present day systems are examined and their performance is analyzed. In a third phase are pointed out the major areas for research and development that are presently being followed in the construction of legged robots. Finally, some of the problems still unsolved, that remain defying robotics research, are also addressed.N/
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