An Improved Active SLAM Algorithm for Multi-Robot Exploration

1. An Improved Active SLAM Algorithm for Multi-Robot Exploration Viet-Cuong Pham and Jyh-Ching Juang National Cheng Kung University, Taiwan September 16, 2011 SICE 2011 Tokyo, Japan Supported by: Taiwan National Science Council Naional Cheng Kung University

2. Outline • Introduction • Active SLAM problem with multiple robots • Proposed approach • General framework • Exploration phase with global optimization strategy • Relocalization phase • Adaptive uncertainty threshold • Limited communication range • Simulations and Discussions • Conclusions

3. 1. Introduction • Exploration • Robots cover the environment with their sensors • SLAM (Simultaneous Localization and Mapping) • A mobile robot attempts to build a map • At the same time uses this map to deduce its location • Ordinary SLAMs • Only process perceived sensor data • Do not influence the motion of the mobile robot • Path control strategy can have a substantial impact on the quality of the resulting map • This study: blend concepts of SLAM, active path • planning, and cooperation among multiple robots

4. 1. Introduction Comparison of different exploration methods

5. 2. Active SLAM problem with multiple robots The state vector : pose of the r-th robot at time k : position of the l-th landmark

6. 2. Active SLAM problem with multiple robots • Process model: • Measurement model:

7. 2. Active SLAM problem with multiple robots • Objectives: • Cover the whole environment in a minimum amount of time • Guarantee the accuracy of the map • Constrained optimization problem • : pose error covariance of the r-th robot • α: predefined threshold

8. 3. Proposed approach General framework • Two parts: • Path generation: determining a target point for each robot • SLAM operation • Two-phase process

9. 3. Proposed approach Exploration phase • Frontier-based exploration • Existing method: • Disperse robots at local level • Assume a priori knowledge of the environment • Proposed: Global optimization strategy • Globally disperse robots • Do not assume a priori knowledge of the environment

10. 3. Proposed approach Exploration phase • Objective function • : utility of information • : utility of localizability (distinguish between • target points with different localization quality) • : cost of navigation • : weighting factors • : penalty coefficient • : assignment matrix • Determine A: integer programming problem

11. 3. Proposed approach Relocalization phase • Objective: maintain the accuracy of the map • Robot switches to relocalization phase when pose uncertainty becomes large • Revisit previously seen landmarks • Rendezvous with other robots

12. 3. Proposed approach Relocalization phase • Objective function • : utility of localizability • : cost of navigation • : loss due to interruption of exploration task (if • other robots involved) • : distance to the nearest exploration point • : weighting factors

13. 3. Proposed approach Relocalization phase • r-th robot: revisit A or meet s-th robot at C? • D: to assess the effort needed to go back to perform exploration A

14. 3. Proposed approach Adaptive uncertainty threshold • Fixed threshold α: robots may get stuck in regions with few or no landmarks • Repeatedly switching between exploration and relocalization phases

15. 3. Proposed approach Adaptive uncertainty threshold • Temporary increase the uncertainty threshold • Threshold should be reduced asap: avoid large error in the exploration process afterwards. • When to reduce? • Robot pose uncertainty decreases • Landmark density is high

16. 3. Proposed approach Limited communication range • Keep robots within communication range? • Robots are allowed to temporary move out of the communication range and rendezvous later • Objective function for choosing rendezvous point • : utility of localizability • : cost of navigation • : distance to the nearest exploration point • : weighting factors

17. 4. Simulations & Discussions • Noise covariance matrices • Destination is recomputed whenever a robot reached its current destination or a robot has moved 3 m • Control signals: applied every 0.025 s • Range and bearing measurements: taken every 0.2s • Mean velocity of the robots: 3 m/s • Sensor range: 20 m • No. of runs: 20

18. 4. Simulations & Discussions Global optimization strategy • Case A (proposed): global optimization strategy • Case B: without global optimization strategy

19. 4. Simulations & Discussions Global optimization strategy 64.6%

20. 4. Simulations & Discussions Adaptive uncertainty threshold A snap-shot of an unfinished exploration mission with fixed uncertainty threshold (Case C)

21. 4. Simulations & Discussions Limited communication range

22. 4. Simulations & Discussions Limited communication range Case G: robots are allowed to move out of communication, no rendezvous Case H: robots are allowed to move out of communication and rendezvous later

23. 5. Conclusions • An improved active SLAM algorithm for multi-robot exploration is presented • Three important improvements: • A global optimization strategy in the exploration phase • An adaptive strategy: automatically adjust the threshold of the robot pose uncertainty constraints • Rendezvous technique: deal with the limited communication range problem • Improved approach outperforms the original one

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26. References Thank you for your attention!