[ecoop-info] PhD Position at CIAD / Technology University of Belfort-Montbaliard on "Contribution to cooperative perception and cooperative planning and control of robots in uncertain environment"

[Stéphane Galland]

The CIAD laboratory is searching for a PhD candidate to work on
cooperative control and planning, cooperative environmental
perception,machine learning, multi-agent systems and robot behavior
analysis.

The PhD project is funded by the Technology University of Belfort-
Montbeliard for 36 months.Starting period is September/October 2021.

Application details are into the attached file or available on 
https://www.linkedin.com/feed/update/urn:li:activity:6794917064424341505/



1) Introduction / background:
Several applications in the field of robotics require interactions
between robots toaccomplish their task. These interactions can be conflictual as in the
case of space sharing,or collaborative as during handling operations. The movements of the
robots in both casesmust be synchronized to perform their tasks safely. Due to the
uncertain environment,especially in the presence of humans, these movements can experience
delays, hence theneed to share the perception of the environment. There are two possible
solutions tomeet this need. The first one consists of building a global dynamic
representation mapshared and updated by all robots. This assumes that it must be managed
centrally. In thesecond approach, which is decentralized, the robots communicate
interfering elementswith each other. To do this, they must be able to classify the states
of the environmentand jointly define the different sources of delay to synchronize
accordingly. Two scientificbuilding blocks are identified in the proposed thesis subject.Cooperative planning and control: This involves studying interaction
models and analyzing theproperties of control or trajectory planning. In addition to the
properties of the solutions, themodel will be used to deduce the relevant information to be exchanged
between the robots.Other control or planning techniques can be exploited. Through these
analyzes, the student will beable to address the thorny issue of multi-agent reinforcement learning
in the context ofcontinuous decision-making [1]. The aim here is to test the potentials
of Deep ReinforcementLearning (DRL) in the context of the learning of several agents [2].
Also, other distributed controlstrategies can be deduced, explored and compared.Dynamic cooperative perception: This involves sharing the perception of
a robot's environmentwith other robots and vice versa in a collaborative context [3]. The
objective is to increase theperception of each of the robots in order to offer them broader
perspectives to carry out theirindividual and collective tasks as well as possible [4]. In general,
each robot, equipped with one ormore sensors (cameras, Lidars, etc.), must be able to locally perceive
its surrounding space, thenintegrate all the information useful for the mission of each robot in
its perception or knowledgemap [5]. The student will focus particularly on creating a dynamic
representation of the perceptionof each robot by exploiting its own perception and those shared by
other robots. The objectivehere is to understand the dynamic content of the environment by
recognizing situations or eventsthat may cause difficulties to the robot itself, but also to other
robots participating in the collectivemission. This representation requires a spatial and temporal
registration, which can be complexdepending on the type of information shared.2) Planned works:The two scientific topics presented above will have to be treated and
exploited jointly.Cooperative control and planning can benefit fromdynamic cooperative
perception and vice versa.Indeed, the results of perception will be exploited to optimize the
control of the robots, and inreturn, the perception process will exploit the robots control or
planning to improve theirperception in terms of prediction for example. From a practical point
of view, the sharing andupdating of the perception map of each robot can be done at the request
of the robot concerned(to other robots) or can be detected automatically as part of a
strategy defined by the missionitself and made known to all robots participating in the mission.For experiment and testing, the student will benefit from an
application in a concrete case ofcollaboration between several real robots and a computing platform. The
data will be generatedthrough real and augmented tests.
[1] Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, and Igor
Mordatch. 2017. Multi-agentactor-critic for mixed cooperative-competitive environments. In
Proceedings of the 31stInternational Conference on Neural Information Processing Systems
(NIPS'17). Curran AssociatesInc., Red Hook, NY, USA, 6382–6393.[2] OROOJLOOYJADID, Afshin et HAJINEZHAD, Davood. A review of
cooperative multi-agent deepreinforcement learning. arXiv preprint arXiv:1908.03963, 2019.[3] SCHMUCK, Patrik et CHLI, Margarita. CCM ‐ SLAM: Robust and
efficient centralized collaborativemonocular simultaneous localization and mapping for robotic teams.
Journal of Field Robotics,2019, vol. 36, no 4, p. 763-781.[4] QUERALTA, Jorge Pena, TAIPALMAA, Jussi, PULLINEN, Bilge Can, et al.
Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active
Vision. IEEE Access,2020, vol. 8, p. 191617-191643.[5] YANG, Chule, WANG, Danwei, ZENG, Yijie, et al. Knowledge-based
multimodal informationfusion for role recognition and situation assessment by using mobile
robot. Information Fusion,2019, vol. 50, p. 126-138.-- 




	
		
		
			Laboratoire Connaissance et Intelligence Artificielle Distribuées

			CIAD UMR 7533
		
	
	
		
		
			Prof. Dr. Stéphane GALLAND

			Full Professor of Computer Science and Multiagent Systems

		
	
	
		
		
			Deputy Director of CIAD

			French Head of ARFITEC ARF-17-11 & ARF-19-11 "Energy, Transport, Industry, Challenges for tomorrow"

			Senior member of the Multiagent Group

			Member of AFIA
		
	
	
		
		
			Université de Technologie de Belfort-Montbéliard - UBFC

			13, rue Ernest Thierry-Mieg

			90010 Belfort Cedex, FRANCE
		
	
	
		
		
			CIAD Lab: www.ciad-lab.fr

			Web: www.ciad-lab.fr/author-10836

			Phone: +33 384 583 418 (work office)