4,503 research outputs found
Path Planning Tolerant to Degraded Locomotion Conditions
Mobile robots, especially those driving outdoors and in unstructured terrain,
sometimes suffer from failures and errors in locomotion, like unevenly
pressurized or flat tires, loose axes or de-tracked tracks. Those are errors
that go unnoticed by the odometry of the robot. Other factors that influence
the locomotion performance of the robot, like the weight and distribution of
the payload, the terrain over which the robot is driving or the battery charge
could not be compensated for by the PID speed or position controller of the
robot, because of the physical limits of the system. Traditional planning
systems are oblivious to those problems and may thus plan unfeasible
trajectories. Also, the path following modules oblivious to those problems will
generate sub-optimal motion patterns, if they can get to the goal at all.
In this paper, we present an adaptive path planning algorithm that is
tolerant to such degraded locomotion conditions. We do this by constantly
observing the executed motions of the robot via simultaneously localization and
mapping (SLAM). From the executed path and the given motion commands, we
constantly on the fly collect and cluster motion primitives (MP), which are in
turn used for planning. Therefore the robot can automatically detect and adapt
to different locomotion conditions and reflect those in the planned paths
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
Keep Rollin' - Whole-Body Motion Control and Planning for Wheeled Quadrupedal Robots
We show dynamic locomotion strategies for wheeled quadrupedal robots, which
combine the advantages of both walking and driving. The developed optimization
framework tightly integrates the additional degrees of freedom introduced by
the wheels. Our approach relies on a zero-moment point based motion
optimization which continuously updates reference trajectories. The reference
motions are tracked by a hierarchical whole-body controller which computes
optimal generalized accelerations and contact forces by solving a sequence of
prioritized tasks including the nonholonomic rolling constraints. Our approach
has been tested on ANYmal, a quadrupedal robot that is fully torque-controlled
including the non-steerable wheels attached to its legs. We conducted
experiments on flat and inclined terrains as well as over steps, whereby we
show that integrating the wheels into the motion control and planning framework
results in intuitive motion trajectories, which enable more robust and dynamic
locomotion compared to other wheeled-legged robots. Moreover, with a speed of 4
m/s and a reduction of the cost of transport by 83 % we prove the superiority
of wheeled-legged robots compared to their legged counterparts.Comment: IEEE Robotics and Automation Letter
Autonomous Locomotion Mode Transition Simulation of a Track-legged Quadruped Robot Step Negotiation
Multi-modal locomotion (e.g. terrestrial, aerial, and aquatic) is gaining
increasing interest in robotics research as it improves the robots
environmental adaptability, locomotion versatility, and operational
flexibility. Within the terrestrial multiple locomotion robots, the advantage
of hybrid robots stems from their multiple (two or more) locomotion modes,
among which robots can select from depending on the encountering terrain
conditions. However, there are many challenges in improving the autonomy of the
locomotion mode transition between their multiple locomotion modes. This work
proposed a method to realize an autonomous locomotion mode transition of a
track-legged quadruped robot steps negotiation. The autonomy of the
decision-making process was realized by the proposed criterion to comparing
energy performances of the rolling and walking locomotion modes. Two climbing
gaits were proposed to achieve smooth steps negotiation behaviours for energy
evaluation purposes. Simulations showed autonomous locomotion mode transitions
were realized for negotiations of steps with different height. The proposed
method is generic enough to be utilized to other hybrid robots after some
pre-studies of their locomotion energy performances
The kinematics of hyper-redundant robot locomotion
This paper considers the kinematics of hyper-redundant (or âserpentineâ) robot locomotion over uneven solid terrain, and presents algorithms to implement a variety of âgaitsâ. The analysis and algorithms are based on a continuous backbone curve model which captures the robot's macroscopic geometry. Two classes of gaits, based on stationary waves and traveling waves of mechanism deformation, are introduced for hyper-redundant robots of both constant and variable length. We also illustrate how the locomotion algorithms can be used to plan the manipulation of objects which are grasped in a tentacle-like manner. Several of these gaits and the manipulation algorithm have been implemented on a 30 degree-of-freedom hyper-redundant robot. Experimental results are presented to demonstrate and validate these concepts and our modeling assumptions
Planning Hybrid Driving-Stepping Locomotion on Multiple Levels of Abstraction
Navigating in search and rescue environments is challenging, since a variety
of terrains has to be considered. Hybrid driving-stepping locomotion, as
provided by our robot Momaro, is a promising approach. Similar to other
locomotion methods, it incorporates many degrees of freedom---offering high
flexibility but making planning computationally expensive for larger
environments.
We propose a navigation planning method, which unifies different levels of
representation in a single planner. In the vicinity of the robot, it provides
plans with a fine resolution and a high robot state dimensionality. With
increasing distance from the robot, plans become coarser and the robot state
dimensionality decreases. We compensate this loss of information by enriching
coarser representations with additional semantics. Experiments show that the
proposed planner provides plans for large, challenging scenarios in feasible
time.Comment: In Proceedings of IEEE International Conference on Robotics and
Automation (ICRA), Brisbane, Australia, May 201
Body Lift and Drag for a Legged Millirobot in Compliant Beam Environment
Much current study of legged locomotion has rightly focused on foot traction
forces, including on granular media. Future legged millirobots will need to go
through terrain, such as brush or other vegetation, where the body contact
forces significantly affect locomotion. In this work, a (previously developed)
low-cost 6-axis force/torque sensing shell is used to measure the interaction
forces between a hexapedal millirobot and a set of compliant beams, which act
as a surrogate for a densely cluttered environment. Experiments with a
VelociRoACH robotic platform are used to measure lift and drag forces on the
tactile shell, where negative lift forces can increase traction, even while
drag forces increase. The drag energy and specific resistance required to pass
through dense terrains can be measured. Furthermore, some contact between the
robot and the compliant beams can lower specific resistance of locomotion. For
small, light-weight legged robots in the beam environment, the body motion
depends on both leg-ground and body-beam forces. A shell-shape which reduces
drag but increases negative lift, such as the half-ellipsoid used, is suggested
to be advantageous for robot locomotion in this type of environment.Comment: First three authors contributed equally. Accepted to ICRA 201
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