Cooperative localization and control for multi-robot manipulation

Spletzer, John; Das, Arindam; Fierro, Rafael; Taylor, Camillo J.; Kumar, Vijay; Ostrowski, James

doi:10.1109/iros.2001.976240

Cited by 146 publications

(85 citation statements)

References 18 publications

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“…Another advantage of our framework is that using shape and its dynamics makes the representation invariant to translation, in-plane rotation or sensor zoom. The idea of using "shape" to model activities performed by groups of moving objects is similar to recent work in literature on controlling formations of groups of robots using shape (e.g., [5]). …”

mentioning

confidence: 94%

"Shape Activity": a continuous-state HMM for moving/deforming shapes with application to abnormal activity detection

Vaswani

Roy-Chowdhury

Chellappa

2005

IEEE Trans. on Image Process.

124

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Abstract-The aim is to model "activity" performed by a group of moving and interacting objects (which can be people, cars, or different rigid components of the human body) and use the models for abnormal activity detection. Previous approaches to modeling group activity include co-occurrence statistics (individual and joint histograms) and dynamic Bayesian networks, neither of which is applicable when the number of interacting objects is large. We treat the objects as point objects (referred to as "landmarks") and propose to model their changing configuration as a moving and deforming "shape" (using Kendall's shape theory for discrete landmarks). A continuous-state hidden Markov model is defined for landmark shape dynamics in an activity. The configuration of landmarks at a given time forms the observation vector, and the corresponding shape and the scaled Euclidean motion parameters form the hidden-state vector. An abnormal activity is then defined as a change in the shape activity model, which could be slow or drastic and whose parameters are unknown. Results are shown on a real abnormal activity-detection problem involving multiple moving objects.

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mentioning

confidence: 94%

"Shape Activity": a continuous-state HMM for moving/deforming shapes with application to abnormal activity detection

Vaswani

Roy-Chowdhury

Chellappa

2005

IEEE Trans. on Image Process.

124

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“…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

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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.

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“…For example, in military missions, aerial vehicles fly in formations for mutual defense and reconnaissance; in transportation, vehicle platooning saves fuel and increases the throughput of transportation networks [1]; and in cooperative object manipulation tasks, multiple robots form a formation in order to move a large payload [2].…”

Section: Introductionmentioning

confidence: 99%

Optimized motion strategies for localization in leader-follower formations

Zhou¹,

Roumeliotis²

2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-This paper addresses the problem of determining the optimal robot trajectory for localizing a robot follower in a leader-follower formation using robot-to-robot distance or bearing measurements. In particular, maintaining a perfect formation has been shown to reduce the localization accuracy (as compared to moving randomly), or even leads to loss of observability when only distance or bearing measurements are available and the robots move on parallel straight lines. To address this limitation, we allow the follower to slightly deviate from its desired formation-imposed position and seek to find the next best location where it should move to in order to minimize the uncertainty about its relative, with respect to the leader, position and orientation estimates. We formulate and solve this non-convex optimization problem analytically and show, through extensive simulations, that the proposed optimized motion strategy leads to significant localization accuracy improvement as compared to competing approaches.

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Cooperative localization and control for multi-robot manipulation

Cited by 146 publications

References 18 publications

"Shape Activity": a continuous-state HMM for moving/deforming shapes with application to abnormal activity detection

"Shape Activity": a continuous-state HMM for moving/deforming shapes with application to abnormal activity detection

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

Optimized motion strategies for localization in leader-follower formations

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