Motion planning of multiple mobile robots for cooperative manipulation and transportation

Yamashita, Atsushi; Arai, Tamio; Ota, Jun; Asama, Hajime

doi:10.1109/tra.2003.809592

Cited by 165 publications

(85 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, cooperative construction [98], [99] requires formation-like motion as well as specialized low-level motions for stabilizing and manipulating objects. In such cases, physics-based models for manipulation can be solved to find a relative motion plan for the robots, while a global path plan can be constructed using any of the well-known path planning algorithms [98].…”

Section: A Trajectory Generation and Motion Planning For Swarmsmentioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

View full text Add to dashboard Cite

Abstract-The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional (3-D) space, and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of the paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas.

show abstract

Section: A Trajectory Generation and Motion Planning For Swarmsmentioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

View full text Add to dashboard Cite

show abstract

“…The literature has approached the underlying problems separately. Many methods have been proposed for collaborative manipulation/transport of objects by a team of robots [9,12,15,23,25]. Particularly, Desai and Kumar [9] propose a motion planning approach for a team of robots transporting an object among obstacles; and Khatib et al [12] present a decentralized control framework for the manipulation of an object with a system of multiple manipulators.…”

Section: For Precise Detailsmentioning

confidence: 99%

Towards Coordinated Precision Assembly with Robot Teams

Dogar¹,

Knepper²,

Spielberg³

et al. 2015

Experimental Robotics

View full text Add to dashboard Cite

Abstract. We present a system in which a flexible team of robots coordinate to assemble large, complex, and diverse structures autonomously. Our system operates across a wide range of spatial scales and tolerances, using a hierarchical perception architecture. For the successful execution of very precise assembly operations under initial uncertainty, our system starts with high-field of view but low accuracy sensors, and gradually use low field-of-view but high accuracy sensors. Our system also uses a failure detection and recovery system, integrated with this hierarchical perception architecture: upon losing track of a feature, our system retracts to using high-field of view systems to re-localize. Additionally, we contribute manipulation skills and tools necessary to assemble large structures with high precision. First, the team of robots coordinate to transport large assembly parts which are too heavy for a single robot to carry. Second, we develop a new tool which is capable of co-localizing holes and fasteners for robust insertion and fastening. We present real robot experiments where we measure the contribution of the hierarchical perception and failure recovery approach to the robustness of our system. We also present an extensive set of experiments where our robots successfully insert all 80 of the attempted fastener insertion operations.

show abstract

“…Some tasks cannot be accomplished by one robot, such as the transport of an object too large for a single robot to move [24,76,91,101]. Other issues, such as the dynamic allocation of tasks to robots [4,5,16,60,64,87,96], simply do not arise with a single robot.…”

Section: Related Workmentioning

confidence: 99%

Designing the HRTeam Framework: Lessons Learned from a Rough-and-Ready Human/Multi-Robot Team

Sklar

Ozgelen

Muñoz

et al. 2012

Advanced Agent Technology

View full text Add to dashboard Cite

Abstract. In this workshop paper, we share the design and on-going implementation of our HRTeam framework, which is constructed to support multiple robots working with a human operator in a dynamic environment. The team is comprised of one human plus a heterogeneous set of inexpensive, limited-function robots. Although each individual robot has restricted mobility and sensing capabilities, together the team members constitute a multi-function, multi-robot facility. We describe low-level system architecture details and explain how we have integrated a popular robotic control and simulation environment into our framework to support application of multi-agent techniques in a hardware-based environment. We highlight lessons learned regarding the integration of multiple varying robot platforms into our system, from both hardware and software perspectives. Our aim is to generate discussion amongst multirobot researchers concerning issues that are of particular interest and present particular difficulties to the multi-robot systems community.

show abstract

Motion planning of multiple mobile robots for cooperative manipulation and transportation

Cited by 165 publications

References 40 publications

A Survey on Aerial Swarm Robotics

A Survey on Aerial Swarm Robotics

Towards Coordinated Precision Assembly with Robot Teams

Designing the HRTeam Framework: Lessons Learned from a Rough-and-Ready Human/Multi-Robot Team

Contact Info

Product

Resources

About