Decentralized Approaches for Robot Fleet Control
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1 Workshop on AERIAL ROBOTICS - Onera Toulouse 2-3 October 2014 Decentralized Approaches for Robot Fleet Control INSA Lyon CITI-Inria Lab. - Dynamid team Olivier.Simonin@insa-lyon.fr
2 Outline I. Decentralized control/decision Needs, problems and approaches II. Multi-robot cooperation Illustration with the CAROTTE Challenge How to decentralized? III. Towards bio-inspired control Motivations The EVAP model Application with simulated UAVS
3 Outline I. Decentralized control/decision Needs, problems and approaches II. Multi-robot cooperation Illustration with the CAROTTE Challenge How to decentralized? III. Towards bio-inspired control Motivations The EVAP model Application with simulated UAVS
4 Introduction Evolution of ''modern'' autonomous (mobile) robots Cybernetics (~1948) swarm robotics autonomous mobile robot 1970 multi-robot systems 1990 connected objects Networked robots
5 Introduction Evolution of ''modern'' autonomous (mobile) robots A.I. & Control theory Distributed A.I. (& bio-insp.) Cybernetics (~1948) swarm robotics autonomous mobile robot 1970 multi-robot systems 1990 Grid. & network Comput. connected objects Networked robots
6 Introduction Complexity with the number of robots # states # messsages planning time Networked robots single mobile robot 1 Multi-robot systems No / local comm. Swarm robotics >100 # robots 6
![Introduction Complexity of Problems Multi-robot path planning Exponential in # robots Depends also on env. complexity, cf. [Parker] Cooperative tasks in unknown env.](/docs-images/76/73443237/images/7-3.jpg "Coverage Mapping Patrolling Tracking etc. CollMot project ERC, Pennsylvania 2012 Motion coordination Coordination (trafic pb.) Swarming (cf. [J.-M.")
7 Introduction Complexity of Problems Multi-robot path planning Exponential in # robots Depends also on env. complexity, cf. [Parker] Cooperative tasks in unknown env. Coverage Mapping Patrolling Tracking etc. CollMot project ERC, Pennsylvania 2012 Motion coordination Coordination (trafic pb.) Swarming (cf. [J.-M. Moschetta, micro-air vehicles]) Flight formation : cenrtalized vs. decentr. (flocking) [Tlig et al 14] 7
8 Introduction Centralized vs. decentralized approaches Optimisation (centralized) techniques no scalling Requires global information optimal sol. Prob. : seq. decision process (MDP) Sol. : dyn. prog., value/policy iteration memory & time consuming scale up real time Decentralized techniques Local information/decision (heuristics) (Emergent) global behavior Bio-inspired mecanisms (flocking, ACO, pot. fields..) no optimal sol. Challenge : Mixing the approaches 8
9 Outline I. Decentralized control/decision Needs, problems and approaches II. Multi-robot cooperation Illustration with the CAROTTE Challenge How to decentralized? III. Towards bio-inspired control Motivations The EVAP model Application with simulated UAVS
10 Multi-robot SLAM Decentralized decision in real applications? The Carotte Challenge (ANR/DGA) Exploration and mapping with autonomous and communicating robots A 120m² unknowm indoor environment 30' max to finish the mission The Cartomatic project LISA-Angers & MAIA team INRIA Nancy A multi-robot approach (with decentralized decision) Competitiion between 5 french consortiums 10
11 Multi-robot SLAM Multi-robot exploration The Carotte Challenge (ANR/DGA) Exploration and mapping with autonomous and communicating robots A 120m² unknowm indoor environment 30' max to finish the mission The Cartomatic project Frontier-based algorithm Broadcast of navigation map Up to 6 Minirex robots in expe. Kinect and SLAM fn. Exchange pos. & map 11
12 Multi-robot SLAM Standard approach Criteria Optimization only based on robot-frontier distances (Ci j ) n robots m frontiers static view of the problem Example Greedy assignation (eg. [Burgard et al 02]) 2 robots go towards the same area ignored area 12
13 Multi-robot SLAM MinPos : an heuristic for task allocation Principle Introducing a spatial balance : new criteria : rank of a robot in the fleet towards a frontier = nb. of robots which are closer assignation to the frontier which minimize the rank example MinPos 13
14 Multi-robot SLAM MinPos : an heuristic for task allocation Principle Introducing a spatial balance : new criteria : rank of a robot in the fleet towards a frontier = nb. of robots which are closer assignation to the frontier which minimize the rank Executed on each robot 14
15 Multi-robot SLAM Simulation Hospital env. 15
16 Multi-robot SLAM Experiments ﾲ Local decision = F (robots loc.) 2D Map built by 3 robots - trajectories Perf. depends on communication range and assign. frequency [Bautin PhD], [ICIRA 2012] 16
17 Multi-robot SLAM Robustness and efficiency Multi-robot SLAM Cooperation : saving time Robustness to robot failure/danger CAROTTE : First place! (2012) Carotte Challenge Final 2012 (recording) Cartomatic team 17
18 Outline I. Decentralized control/decision Needs, problems and approaches II. Multi-robot cooperation Illustration with the CAROTTE Challenge How to decentralized? III. Towards bio-inspired control Motivations The EVAP model Application with simulated UAVS
19 Swarm robotics Collective navigation and self-configuration Box-pushing 1988 Kilobots (2011) Self-configurable robots (M-TRAN III) 2005 CollMot project (UAV flocking) Pennsylvania
20 The EVAP model EVAP : patrolling with a pheromone-based algo. Patrolling in unknown environments Interaction between 2 systems : Agents and Environment Agent dropping pheromones descent of the gradient Environnement evaporation, diffusion EVAP Model (Maia) [Wagner 00], [Glad 2011] 20
21 The EVAP model EVAP : patrolling with a pheromone-based algo. Patrolling in unknown environments Interaction between 2 systems : Agents and Environment Agent dropping pheromones descent of the gradient Environnement m ' (c)=ρ. m( c) evaporation EVAP Model (Maia) [Wagner 00], [Glad 2011] 21
22 The EVAP model EVAP : patrolling with a pheromone-based algo. Patrolling in unknown environments Interaction between 2 systems : Agents and Environment Agent dropping pheromones descent of the gradient Environnement m ' (c)=ρ. m( c) evaporation EVAP Model (Maia) [Wagner 00], [Glad 2011] m0 22
23 The EVAP model EVAP : patrolling with a pheromone-based algo. Patrolling in unknown environments Interaction between 2 systems : Agents and Environment Agent dropping pheromones descent of the gradient Environnement m ' (c)=ρ. m( c) evaporation EVAP Model (Maia) [Wagner 00], [Glad 2011] 23
24 The EVAP model Local marking/reading global organization Self-organization Convergence? 24
25 The EVAP model Local marking/reading global organization Self-organization Convergence to optimal solutions in exponential time automatic detection -> stochastic modeling : Markov c. [SASO 2009] ECAI'08, SASO'09, AAMAS'10 25
26 Outline I. Decentralized control? Different approaches II. Multi-robot cooperation Illustration with the CAROTTE Challenge How to decentralized? III. Towards bio-inspired control Motivations The EVAP model Application with simulated UAVS
27 EVAP with UAVs Experiments with simulated UAVs Simulators Physics engine : fixed-wing mini UAVs EVAP computation (GPU) PEA SMAART project (06-09) 27
28 EVAP with UAVs How operators can interact with a swarm intel.? Scenarios Patrol with 10 UAVs (Several intruders at different time) Operators the operator can take control of any UAV 8 students of the Naval school (Brest) DGA SUSIE Project 28
29 EVAP with UAVs Analysis of results Patrol efficiency with operators Very limited improvement -3.2 % in average intruders Analysis of the interviews No / few understanding of the swarm behavior by operators Details in [Legras et al., SIMPAR 2008] 29
30 EVAP with mobile robots EVAP : a last experiment with real mobile robots Mobile robots (Khepera) + Interactive table (MAIA design [Simonin et al. ICTAI'10]) IR emitter and color sensors under the robots EVAP properties? EVAP expriment 6 robots 30
31 Conclusion Conclusion & perspectives Open challenges in autonomous robotic fleets Real time control + global optimisation Scalable models (# robot) Experimentation and validation with large fleets communications in robot fleets robustness to failure, dedicated middleware and soft. interaction Human Fleet new way of interaction new behavior representation.. 31
32 The end Thank you for your attention! News! European Open Robocup in Lyon, march
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