53,251 research outputs found
Manipulation Planning and Control for Shelf Replenishment
Manipulation planning and control are relevant building blocks of a robotic
system and their tight integration is a key factor to improve robot autonomy
and allows robots to perform manipulation tasks of increasing complexity, such
as those needed in the in-store logistics domain. Supermarkets contain a large
variety of objects to be placed on the shelf layers with specific constraints,
doing this with a robot is a challenge and requires a high dexterity. However,
an integration of reactive grasping control and motion planning can allow
robots to perform such tasks even with grippers with limited dexterity. The
main contribution of the paper is a novel method for planning manipulation
tasks to be executed using a reactive control layer that provides more control
modalities, i.e., slipping avoidance and controlled sliding. Experiments with a
new force/tactile sensor equipping the gripper of a mobile manipulator show
that the approach allows the robot to successfully perform manipulation tasks
unfeasible with a standard fixed grasp.Comment: 8 pages, 12 figures, accepted at RA
Reactive Base Control for On-The-Move Mobile Manipulation in Dynamic Environments
We present a reactive base control method that enables high performance
mobile manipulation on-the-move in environments with static and dynamic
obstacles. Performing manipulation tasks while the mobile base remains in
motion can significantly decrease the time required to perform multi-step
tasks, as well as improve the gracefulness of the robot's motion. Existing
approaches to manipulation on-the-move either ignore the obstacle avoidance
problem or rely on the execution of planned trajectories, which is not suitable
in environments with dynamic objects and obstacles. The presented controller
addresses both of these deficiencies and demonstrates robust performance of
pick-and-place tasks in dynamic environments. The performance is evaluated on
several simulated and real-world tasks. On a real-world task with static
obstacles, we outperform an existing method by 48\% in terms of total task
time. Further, we present real-world examples of our robot performing
manipulation tasks on-the-move while avoiding a second autonomous robot in the
workspace. See https://benburgesslimerick.github.io/MotM-BaseControl for
supplementary materials
Reactive Planning for Mobile Manipulation Tasks in Unexplored Semantic Environments
Complex manipulation tasks, such as rearrangement planning of numerous objects, are combinatorially hard problems. Existing algorithms either do not scale well or assume a great deal of prior knowledge about the environment, and few offer any rigorous guarantees. In this paper, we propose a novel hybrid control architecture for achieving such tasks with mobile manipulators. On the discrete side, we enrich a temporal logic specification with mobile manipulation primitives such as moving to a point, and grasping or moving an object. Such specifications are translated to an automaton representation, which orchestrates the physical grounding of the task to mobility or manipulation controllers. The grounding from the discrete to the continuous reactive controller is online and can respond to the discovery of unknown obstacles or decide to push out of the way movable objects that prohibit task accomplishment. Despite the problem complexity, we prove that, under specific conditions, our architecture enjoys provable completeness on the discrete side, provable termination on the continuous side, and avoids all obstacles in the environment. Simulations illustrate the efficiency of our architecture that can handle tasks of increased complexity while also responding to unknown obstacles or unanticipated adverse configurations.
For more information: Kod*la
Analysis and Observations from the First Amazon Picking Challenge
This paper presents a overview of the inaugural Amazon Picking Challenge
along with a summary of a survey conducted among the 26 participating teams.
The challenge goal was to design an autonomous robot to pick items from a
warehouse shelf. This task is currently performed by human workers, and there
is hope that robots can someday help increase efficiency and throughput while
lowering cost. We report on a 28-question survey posed to the teams to learn
about each team's background, mechanism design, perception apparatus, planning
and control approach. We identify trends in this data, correlate it with each
team's success in the competition, and discuss observations and lessons learned
based on survey results and the authors' personal experiences during the
challenge
PIWeCS: enhancing human/machine agency in an interactive composition system
This paper focuses on the infrastructure and aesthetic approach used in PIWeCS: a Public Space Interactive Web-based Composition System. The concern was to increase the sense of dialogue between human and machine agency in an interactive work by adapting Paine's (2002) notion of a conversational model of interaction as a ‘complex system’. The machine implementation of PIWeCS is achieved through integrating intelligent agent programming with MAX/MSP. Human input is through a web infrastructure. The conversation is initiated and continued by participants through arrangements and composition based on short performed samples of traditional New Zealand Maori instruments. The system allows the extension of a composition through the electroacoustic manipulation of the source material
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