239 research outputs found
The Disruptive Technology That is Additive Construction: System Development Lessons Learned for Terrestrial and Planetary Applications
Disruptive technologies are unique in that they spawn other new technologies and applications as they grow. These activities are usually preceded by the question, "What If?" For example, "What if we could use an emerging technology and in-situ materials to promote exploration on the Moon or Mars, and then use that same technology to keep our troops out of harm's way and/or help the worlds' homeless?" This question allows us to flip the mindset of "how can people create more valuable innovation?" to "how can innovation create more valuable people?." This approach allows us to view augmented human labor as an inclusive opportunity, not a threat. The discipline of Additive Construction is growing rapidly due to the flexibility, speed, safety and logistics benefits offered as compared to standard construction techniques. Additive construction is a disruptive technology in that it employs the principles of additive manufacturing on a human habitat structure scale. Developed initially for emergency management and disaster relief applications, additive construction has now grown into military infrastructure and planetary (Moon and Mars) surface infrastructure applications as well. Additive Construction with Mobile Emplacement (ACME) is a NASA technology development project that seeks to demonstrate the feasibility of constructing shelters for human crews, and other surface infrastructure, on the Moon or Mars for a future human presence. The ACME project will allow, for the first time, the 3-dimensional printing of surface structures on planetary bodies using local materials for construction, thereby tremendously reducing launch and transportation mass and logistics. Some examples of infrastructure that could be constructed using robotic additive construction methods are landing pads, rocket engine blast protection berms, roads, dust free zones, equipment shelters, habitats and radiation shelters. Terrestrial applications include the development of surface structures using Earth-based materials for emergency response, disaster relief, general construction, and housing at all economic levels. This paper will describe the progress made by the NASA ACME project with a focus on prototypes and full scale additive construction demonstrations using both Portland cement concrete and other indigenous material mixtures. Rationale for the use of additive construction for both terrestrial and planetary applications will be explored and a thorough state-of-the-art of additive construction techniques will be presented. An evolutionary history of NASA's additive construction development efforts, dating back to 2004, will be included. The paper will then step through a series of trade studies performed to inform key processing and design decisions in the development of the full-scale ACES-3 system developed by NASA and the Jacobs Space Exploration Group for the U.S. Army Corps of Engineers (USACE) Construction Engineers Research Laboratory (CERL) in Champaign, IL. The selection of aggregate and binders, based on in-situ materials, will also be presented and discusse
A lightweight tile structure integrating photovoltaic conversion and RF power transfer for space solar power applications
We demonstrate the development of a prototype lightweight (1.5 kg/m^3) tile structure capable of photovoltaic solar power capture, conversion to radio frequency power, and transmission through antennas. This modular tile can be repeated over an arbitrary area to forma large aperture which could be placed in orbit to collect sunlight and transmit electricity to any location. Prototype design is described and validated through finite element analysis, and high-precision ultra-light component manufacture and robust assembly are described
A lightweight tile structure integrating photovoltaic conversion and RF power transfer for space solar power applications
We demonstrate the development of a prototype lightweight (1.5 kg/m^3) tile structure capable of photovoltaic solar power capture, conversion to radio frequency power, and transmission through antennas. This modular tile can be repeated over an arbitrary area to forma large aperture which could be placed in orbit to collect sunlight and transmit electricity to any location. Prototype design is described and validated through finite element analysis, and high-precision ultra-light component manufacture and robust assembly are described
Petri Net Plans A framework for collaboration and coordination in multi-robot systems
Programming the behavior of multi-robot systems is a challenging task which has a key role in developing effective systems in many application domains. In this paper, we present Petri Net Plans (PNPs), a language based on Petri Nets (PNs), which allows for intuitive and effective robot and multi-robot behavior design. PNPs are very expressive and support a rich set of features that are critical to develop robotic applications, including sensing, interrupts and concurrency. As a central feature, PNPs allow for a formal analysis of plans based on standard PN tools. Moreover, PNPs are suitable for modeling multi-robot systems and the developed behaviors can be executed in a distributed setting, while preserving the properties of the modeled system. PNPs have been deployed in several robotic platforms in different application domains. In this paper, we report three case studies, which address complex single robot plans, coordination and collaboration
A flexible coupling approach to multi-agent planning under incomplete information
The final publication is available at Springer via http://dx.doi.org/10.1007/s10115-012-0569-7Multi-agent planning (MAP) approaches are typically oriented at solving loosely
coupled problems, being ineffective to deal with more complex, strongly related problems.
In most cases, agents work under complete information, building complete knowledge
bases. The present article introduces a general-purpose MAP framework designed to tackle
problems of any coupling levels under incomplete information. Agents in our MAP model
are partially unaware of the information managed by the rest of agents and share only the critical
information that affects other agents, thus maintaining a distributed vision of the task.
Agents solve MAP tasks through the adoption of an iterative refinement planning procedure
that uses single-agent planning technology. In particular, agents will devise refinements
through the partial-order planning paradigm, a flexible framework to build refinement plans
leaving unsolved details that will be gradually completed by means of new refinements. Our
proposal is supported with the implementation of a fully operative MAP system and we show
various experiments when running our system over different types of MAP problems, from
the most strongly related to the most loosely coupled.This work has been partly supported by the Spanish MICINN under projects Consolider Ingenio 2010 CSD2007-00022 and TIN2011-27652-C03-01, and the Valencian Prometeo project 2008/051.Torreño Lerma, A.; Onaindia De La Rivaherrera, E.; Sapena Vercher, O. (2014). A flexible coupling approach to multi-agent planning under incomplete information. Knowledge and Information Systems. 38:141-178. https://doi.org/10.1007/s10115-012-0569-7S14117838Argente E, Botti V, Carrascosa C, Giret A, Julian V, Rebollo M (2011) An abstract architecture for virtual organizations: the THOMAS approach. Knowl Inf Syst 29(2):379–403Barrett A, Weld DS (1994) Partial-order planning: evaluating possible efficiency gains. Artif Intell 67(1):71–112Belesiotis A, Rovatsos M, Rahwan I (2010) Agreeing on plans through iterated disputes. In: Proceedings of the 9th international conference on autonomous agents and multiagent systems. pp 765–772Bellifemine F, Poggi A, Rimassa G (2001) JADE: a FIPA2000 compliant agent development environment. In: Proceedings of the 5th international conference on autonomous agents (AAMAS). ACM, pp 216–217Blum A, Furst ML (1997) Fast planning through planning graph analysis. Artif Intell 90(1–2):281–300Boutilier C, Brafman R (2001) Partial-order planning with concurrent interacting actions. J Artif Intell Res 14(105):136Brafman R, Domshlak C (2008) From one to many: planning for loosely coupled multi-agent systems. In: Proceedings of the 18th international conference on automated planning and scheduling (ICAPS). pp 28–35Brenner M, Nebel B (2009) Continual planning and acting in dynamic multiagent environments. J Auton Agents Multiag Syst 19(3):297–331Coles A, Coles A, Fox M, Long D (2010) Forward-chaining partial-order planning. In: Proceedings of the 20th international conference on automated planning and scheduling (ICAPS). pp 42–49Coles A, Fox M, Long D, Smith A (2008) Teaching forward-chaining planning with JavaFF. In: Colloquium on AI education, 23rd AAAI conference on artificial intelligenceCox J, Durfee E, Bartold T (2005) A distributed framework for solving the multiagent plan coordination problem. In: Proceedings of the 4th international joint conference on autonomous agents and multiagent systems (AAMAS). ACM, pp 821–827de Weerdt M, Clement B (2009) Introduction to planning in multiagent systems. Multiag Grid Syst 5(4):345–355Decker K, Lesser VR (1992) Generalizing the partial global planning algorithm. Int J Coop Inf Syst 2(2):319–346desJardins M, Durfee E, Ortiz C, Wolverton M (1999) A survey of research in distributed continual planning. AI Mag 20(4):13–22Doshi P (2007) On the role of interactive epistemology in multiagent planning. In: Artificial intelligence and, pattern recognition. pp 208–213Dréo J, Savéant P, Schoenauer M, Vidal V (2011) Divide-and-evolve: the marriage of descartes and darwin. In: Proceedings of the 7th international planning competition (IPC). Freiburg, GermanyDurfee EH (2001) Distributed problem solving and planning. In: Multi-agents systems and applications: selected tutorial papers from the 9th ECCAI advanced course (ACAI) and agentLink’s third European agent systems summer school (EASSS), vol LNAI 2086. Springer, pp 118–149Durfee EH, Lesser V (1991) Partial global planning: a coordination framework for distributed hypothesis formation. IEEE Trans Syst Man Cybern Special Issue Distrib Sens Netw 21(5):1167–1183Ephrati E, Rosenschein JS (1996) Deriving consensus in multiagent systems. Artif Intell 87(1–2):21–74Fikes R, Nilsson N (1971) STRIPS: a new approach to the application of theorem proving to problem solving. Artif Intell 2(3):189–208Fogués R, Alberola J, Such J, Espinosa A, Garcia-Fornes A (2010) Towards dynamic agent interaction support in open multiagent systems. In: Proceedings of the 2010 conference on artificial intelligence research and development: proceedings of the 13th international conference of the Catalan association for artificial intelligence’. IOS Press, pp 89–98Gerevini A, Long D (2006) Preferences and soft constraints in PDDL3. In: ICAPS workshop on planning with preferences and soft constraints, vol 6. Citeseer, pp 46–53Ghallab M, Howe A, Knoblock C, McDermott D, Ram A, Veloso M, Weld D, Wilkins D (1998) PDDL-the Planning Domain Definition Language. In: AIPS-98 planning committeeGmytrasiewicz P, Doshi P (2005) A framework for sequential planning in multi-agent settings. J Artif Intell Res 24:49–79Haslum P, Jonsson P (1999) Some results on the complexity of planning with incomplete information. In: Proceedings of the 5th European conference on, planning (ECP). pp 308–318Helmert M (2006) The fast downward planning system. J Artif Intell Res 26(1):191–246Hoffmann J, Nebel B (2001) The FF planning system: fast planning generation through heuristic search. J Artif Intell Res 14:253–302Jonsson A, Rovatsos M (2011) Scaling up multiagent planning: a best-response approach. In: Proceedings of the 21st international conference on automated planning and scheduling (ICAPS). AAAI, pp 114–121Kambhampati S (1997) Refinement planning as a unifying framework for plan synthesis. AI Mag 18(2):67–97Kaminka GA, Pynadath DV, Tambe M (2002) Monitoring teams by overhearing: a multi-agent plan-recognition approach. J Artif Intell Res 17:83–135Kone M, Shimazu A, Nakajima T (2000) The state of the art in agent communication languages. Knowl Inf Syst 2(3):259–284Kovacs DL (2011) Complete BNF description of PDDL3.1. Technical reportKraus S (1997) Beliefs, time and incomplete information in multiple encounter negotiations among autonomous agents. Ann Math Artif Intell 20(1–4):111–159Kumar A, Zilberstein S, Toussaint M (2011) Scalable multiagent planning using probabilistic inference. In: Proceedings of the 22nd international joint conference on artificial intelligence (IJCAI)’. Barcelona, Spain, pp 2140–2146Kvarnström J. (2011) Planning for loosely coupled agents using partial order forward-chaining. In: Proceedings of the 21st international conference on automated planning and scheduling (ICAPS). AAAI, pp 138–145Lesser V, Decker K, Wagner T, Carver N, Garvey A, Horling B, Neiman D, Podorozhny R, Prasad M, Raja A et al (2004) Evolution of the GPGP/TAEMS domain-independent coordination framework. Auton Agents Multi Agent Syst 9(1):87–143Lipovetzky N, Geffner H (2011) Searching for plans with carefully designed probes. In: Proceedings of the 21th international conference on automated planning and scheduling (ICAPS)Micacchi C, Cohen R (2008) A framework for simulating real-time multi-agent systems. Knowl Inf Syst 17(2):135–166Nguyen N, Katarzyniak R (2009) Actions and social interactions in multi-agent systems. Knowl Inf Syst 18(2):133–136Nguyen X, Kambhampati S (2001) Reviving partial order planning. In: Proceedings of the 17th international joint conference on artificial intelligence (IJCAI). Morgan Kaufmann, pp 459–464Nissim R, Brafman R, Domshlak C (2010) A general, fully distributed multi-agent planning algorithm. In: Proceedings of the 9th international conference on autonomous agents and multiagent systems (AAMAS). pp 1323–1330Pajares S, Onaindia E (2012) Defeasible argumentation for multi-agent planning in ambient intelligence applications. In: Proceedings of the 11th international conference on autonomous agents and multiagent systems (AAMAS) pp 509–516Paolucci M, Shehory O, Sycara K, Kalp D, Pannu A (2000) A planning component for RETSINA agents. Intelligent Agents VI. Agent Theories Architectures, and Languages pp 147–161Parsons S, Sierra C, Jennings N (1998) Agents that reason and negotiate by arguing. J Logic Comput 8(3):261Penberthy J, Weld D (1992) UCPOP: a sound, complete, partial order planner for ADL. In: Proceedings of the 3rd international conference on principles of knowledge representation and reasoning (KR). Morgan Kaufmann, pp 103–114Richter S, Westphal M (2010) The LAMA planner: guiding cost-based anytime planning with landmarks. J Artif Intell Res 39(1):127–177Sycara K, Pannu A (1998) The RETSINA multiagent system (video session): towards integrating planning, execution and information gathering. In: Proceedings of the 2nd international conference on autonomous agents (Agents). ACM, pp 350–351Tambe M (1997) Towards flexible teamwork. J Artif Intell Res 7:83–124Tang Y, Norman T, Parsons S (2010) A model for integrating dialogue and the execution of joint plans. Argumentation in multi-agent systems, pp 60–78Tonino H, Bos A, de Weerdt M, Witteveen C (2002) Plan coordination by revision in collective agent based systems. Artif Intell 142(2):121–145Van Der Krogt R, De Weerdt M (2005), Plan repair as an extension of planning. In: Proceedings of the 15th international conference on automated planning and scheduling (ICAPS). pp 161–170Weld D (1994) An introduction to least commitment planning. AI Mag 15(4):27Weld D (1999) Recent advances in AI planning. AI Mag 20(2):93–123Wilkins D, Myers K (1998) A multiagent planning architecture. In: Proceedings of the 4th international conference on artificial intelligence planning systems (AIPS), pp 154–162Wu F, Zilberstein S, Chen X (2011) Online planning for multi-agent systems with bounded communication. Artif Intell 175(2):487–511Younes H, Simmons R (2003) VHPOP: versatile heuristic partial order planner. J Artif Intell Res 20: 405–430Zhang J, Nguyen X, Kowalczyk R (2007) Graph-based multi-agent replanning algorithm. In: Proceedings of the 6th conference on autonomous agents and multiagent systems (AAMAS
Predicting the Effect of Surface Texture on the Qualitative Form of Prehension
Reach-to-grasp movements change quantitatively in a lawful (i.e. predictable) manner with changes in object properties. We explored whether altering object texture would produce qualitative changes in the form of the precontact movement patterns. Twelve participants reached to lift objects from a tabletop. Nine objects were produced, each with one of three grip surface textures (high-friction, medium-friction and low-friction) and one of three widths (50 mm, 70 mm and 90 mm). Each object was placed at three distances (100 mm, 300 mm and 500 mm), representing a total of 27 trial conditions. We observed two distinct movement patterns across all trials—participants either: (i) brought their arm to a stop, secured the object and lifted it from the tabletop; or (ii) grasped the object ‘on-the-fly’, so it was secured in the hand while the arm was moving. A majority of grasps were on-the-fly when the texture was high-friction and none when the object was low-friction, with medium-friction producing an intermediate proportion. Previous research has shown that the probability of on-the-fly behaviour is a function of grasp surface accuracy constraints. A finger friction rig was used to calculate the coefficients of friction for the objects and these calculations showed that the area available for a stable grasp (the ‘functional grasp surface size’) increased with surface friction coefficient. Thus, knowledge of functional grasp surface size is required to predict the probability of observing a given qualitative form of grasping in human prehensile behaviour
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