40 research outputs found
Locosim: an Open-Source Cross-Platform Robotics Framework
The architecture of a robotics software framework tremendously influences the
effort and time it takes for end users to test new concepts in a simulation
environment and to control real hardware. Many years of activity in the field
allowed us to sort out crucial requirements for a framework tailored for
robotics: modularity and extensibility, source code reusability, feature
richness, and user-friendliness. We implemented these requirements and
collected best practices in Locosim, a cross-platform framework for simulation
and real hardware. In this paper, we describe the architecture of Locosim and
illustrate some use cases that show its potential.Comment: 12 pages, 4 figures, 1 table, accepted to Clawar 2023, for associated
video see https://youtu.be/ZwV1LEqK-L
An Efficient Paradigm for Feasibility Guarantees in Legged Locomotion
Developing feasible body trajectories for legged systems on arbitrary
terrains is a challenging task. Given some contact points, the trajectories for
the Center of Mass (CoM) and body orientation, designed to move the robot, must
satisfy crucial constraints to maintain balance, and to avoid violating
physical actuation and kinematic limits. In this paper, we present a paradigm
that allows to design feasible trajectories in an efficient manner. In
continuation to our previous work, we extend the notion of the 2D feasible
region, where static balance and the satisfaction of actuation limits were
guaranteed, whenever the projection of the CoM lies inside the proposed
admissible region. We here develop a general formulation of the improved
feasible region to guarantee dynamic balance alongside the satisfaction of both
actuation and kinematic limits for arbitrary terrains in an efficient manner.
To incorporate the feasibility of the kinematic limits, we introduce an
algorithm that computes the reachable region of the CoM. Furthermore, we
propose an efficient planning strategy that utilizes the improved feasible
region to design feasible CoM and body orientation trajectories. Finally, we
validate the capabilities of the improved feasible region and the effectiveness
of the proposed planning strategy, using simulations and experiments on the HyQ
robot and comparing them to a previously developed heuristic approach. Various
scenarios and terrains that mimic confined and challenging environments are
used for the validation.Comment: 17 pages, 13 figures, submitted to Transaction on Robotic
ContactNet: Online Multi-Contact Planning for Acyclic Legged Robot Locomotion
Online trajectory optimization techniques generally depend on heuristic-based
contact planners in order to have low computation times and achieve high
replanning frequencies. In this work, we propose ContactNet, a fast acyclic
contact planner based on a multi-output regression neural network. ContactNet
ranks discretized stepping regions, allowing to quickly choose the best
feasible solution, even in complex environments. The low computation time, in
the order of 1 ms, makes possible the execution of the contact planner
concurrently with a trajectory optimizer in a Model Predictive Control (MPC)
fashion. We demonstrate the effectiveness of the approach in simulation in
different complex scenarios with the quadruped robot Solo12
Fast and Continuous Foothold Adaptation for Dynamic Locomotion through CNNs
Legged robots can outperform wheeled machines for most navigation tasks
across unknown and rough terrains. For such tasks, visual feedback is a
fundamental asset to provide robots with terrain-awareness. However, robust
dynamic locomotion on difficult terrains with real-time performance guarantees
remains a challenge. We present here a real-time, dynamic foothold adaptation
strategy based on visual feedback. Our method adjusts the landing position of
the feet in a fully reactive manner, using only on-board computers and sensors.
The correction is computed and executed continuously along the swing phase
trajectory of each leg. To efficiently adapt the landing position, we implement
a self-supervised foothold classifier based on a Convolutional Neural Network
(CNN). Our method results in an up to 200 times faster computation with respect
to the full-blown heuristics. Our goal is to react to visual stimuli from the
environment, bridging the gap between blind reactive locomotion and purely
vision-based planning strategies. We assess the performance of our method on
the dynamic quadruped robot HyQ, executing static and dynamic gaits (at speeds
up to 0.5 m/s) in both simulated and real scenarios; the benefit of safe
foothold adaptation is clearly demonstrated by the overall robot behavior.Comment: 9 pages, 11 figures. Accepted to RA-L + ICRA 2019, January 201