189 research outputs found
Safe Robotic Grasping: Minimum Impact-Force Grasp Selection
This paper addresses the problem of selecting from a choice of possible
grasps, so that impact forces will be minimised if a collision occurs while the
robot is moving the grasped object along a post-grasp trajectory. Such
considerations are important for safety in human-robot interaction, where even
a certified "human-safe" (e.g. compliant) arm may become hazardous once it
grasps and begins moving an object, which may have significant mass, sharp
edges or other dangers. Additionally, minimising collision forces is critical
to preserving the longevity of robots which operate in uncertain and hazardous
environments, e.g. robots deployed for nuclear decommissioning, where removing
a damaged robot from a contaminated zone for repairs may be extremely difficult
and costly. Also, unwanted collisions between a robot and critical
infrastructure (e.g. pipework) in such high-consequence environments can be
disastrous. In this paper, we investigate how the safety of the post-grasp
motion can be considered during the pre-grasp approach phase, so that the
selected grasp is optimal in terms applying minimum impact forces if a
collision occurs during a desired post-grasp manipulation. We build on the
methods of augmented robot-object dynamics models and "effective mass" and
propose a method for combining these concepts with modern grasp and trajectory
planners, to enable the robot to achieve a grasp which maximises the safety of
the post-grasp trajectory, by minimising potential collision forces. We
demonstrate the effectiveness of our approach through several experiments with
both simulated and real robots.Comment: To be appeared in IEEE/RAS IROS 201
Haptic-guided assisted telemanipulation approach for grasping desired objects from heaps
This paper presents an assisted telemanipulation framework for reaching and
grasping desired objects from clutter. Specifically, the developed system
allows an operator to select an object from a cluttered heap and effortlessly
grasp it, with the system assisting in selecting the best grasp and guiding the
operator to reach it. To this end, we propose an object pose estimation scheme,
a dynamic grasp re-ranking strategy, and a reach-to-grasp hybrid force/position
trajectory guidance controller. We integrate them, along with our previous
SpectGRASP grasp planner, into a classical bilateral teleoperation system that
allows to control the robot using a haptic device while providing force
feedback to the operator. For a user-selected object, our system first
identifies the object in the heap and estimates its full six degrees of freedom
(DoF) pose. Then, SpectGRASP generates a set of ordered, collision-free grasps
for this object. Based on the current location of the robot gripper, the
proposed grasp re-ranking strategy dynamically updates the best grasp. In
assisted mode, the hybrid controller generates a zero force-torque path along
the reach-to-grasp trajectory while automatically controlling the orientation
of the robot. We conducted real-world experiments using a haptic device and a
7-DoF cobot with a 2-finger gripper to validate individual components of our
telemanipulation system and its overall functionality. Obtained results
demonstrate the effectiveness of our system in assisting humans to clear
cluttered scenes.Comment: Accepted to 2023 IEEE International Conference on Systems, Man, and
Cybernetics (SMC
Unsupervised learning-based approach for detecting 3D edges in depth maps
3D edge features, which represent the boundaries between different objects or surfaces in a 3D scene, are crucial for many computer vision tasks, including object recognition, tracking, and segmentation. They also have numerous real-world applications in the field of robotics, such as vision-guided grasping and manipulation of objects. To extract these features in the noisy real-world depth data, reliable 3D edge detectors are indispensable. However, currently available 3D edge detection methods are either highly parameterized or require ground truth labelling, which makes them challenging to use for practical applications. To this extent, we present a new 3D edge detection approach using unsupervised classification. Our method learns features from depth maps at three different scales using an encoder-decoder network, from which edge-specific features are extracted. These edge features are then clustered using learning to classify each point as an edge or not. The proposed method has two key benefits. First, it eliminates the need for manual fine-tuning of data-specific hyper-parameters and automatically selects threshold values for edge classification. Second, the method does not require any labelled training data, unlike many state-of-the-art methods that require supervised training with extensive hand-labelled datasets. The proposed method is evaluated on five benchmark datasets with single and multi-object scenes, and compared with four state-of-the-art edge detection methods from the literature. Results demonstrate that the proposed method achieves competitive performance, despite not using any labelled data or relying on hand-tuning of key parameters.</p
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