A strategy for transporting tall objects with a swarm of miniature mobile robots

Jianing Chen, Melvin Gauci and Roderich Groß

A strategy for transporting tall objects with a swarm of miniature mobile robots
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Abstract

This research proposes a strategy for transporting a tall, and potentially heavy, object to a goal using a large number of miniature mobile robots.

The robots move the object by pushing it, and hence, are not required to have any manipulation capabilities (eg gripping).

The direction in which the object moves is controlled by the way in which the robots distribute themselves around its perimeter. If the robots dynamically reallocate themselves around the section of the object's perimeter that occludes their view of the goal, then the object will eventually be transported to the goal.

This strategy is fully distributed, and makes no use of communication between the robots.

A controller based on this strategy was implemented on a swarm of 12 physical e-puck robots. A systematic experiment with 30 randomised trials was performed. The object was successfully transported to the goal in all the trials.

On average, the path the object was moved was about 7.7% longer than the shortest path.


Highlight video


State flow diagram

State Flow Diagram

In this strategy, making more robots push along the occluded side of the object does not only increase the pushing power, it also stabilises the moving direction of the object. These four movements are the key motions to achieve a uniform and tight pushing formation.

S3. Close-in on object
S4. Scan and align
S5. Push object
S6. Move around object

Motion controller

Block diagram

Motion Controller Block Diagram

Weight matrices

S1. Search object (avoiding)

nS 0.7              
nD 0              
SR -0.25 -0.25 0 0 0 0 -0.25 -0.25
SA -0.125 -0.125 0 0 0 0 -0.125 -0.125
DR 4.25 3.75 1 2 -2 -1 -3.25 -3.5
DA 2.125 1.875 0.5 1 -1 -0.5 -1.625 -1.75

S2. Move to object (avoiding) 

nS 0.7              
nD 0              
SR -0.75 -0.25 0 0 0 0 -0.25 -0.625
SA -0.25 -0.25 0 0 0 0 -0.25 -0.25
DR -0.75 -0.25 0 0 0 0 -0.25 -0.625
DA 4.25 3.75 1 2 -2 -1 -3.25 -3.5

S3. Close in on object (robot avoiding and object following)

nS 0.6              
nD 0              
SR -0.75 -0.25 0 0 0 0 -0.25 -0.625
SA -0.25 -0.25 0 0 0 0 -0.25 -0.25
DR -3.25 -2.25 -1 0 0 0.25 0.75 2.625
DA 4.25 3.75 1 2 -2 -1 -3.25 -3.5

S5. Push object (robot avoiding and object following)

nS 0.5              
nD 0              
SR -0.125 0 0 0 0 0 0 -0.125
SA -0.25 -0.375 0 0 0 0 -0.375 -0.25
DR -1.875 -1 0 0 0 0 1 1.875
DA 1.5 0.125 0 0 0 0 -0.125 -1.5

S6. Move around object (left-hand wall following)

nS 0.1              
nD 0.7              
SR 0 0 0 0 0 2.375 1.5 0
SA 0 0 0 0 0 1.5 0.5 0
DR 0 0 0 0 -1 -3.625 -6 -5
DA 0 0 0 0 -1 -1.5 -2.5 -3

S7. Evade (repulsive motion) 

nS 0              
nD 0              
SR -0.75 -0.375 0 0.625 0.625 0 -0.375 -0.75
SA -0.75 -0.375 0 0.625 0.625 0 -0.375 -0.75
DR 0.25 0.125 0 0.125 -0.125 0 -0.125 -0.25
DA 0.25 0.125 0 0.125 -0.125 0 -0.125 -0.25

Experiment videos

Experiment configuration
Trial 1
Trial 2
Trial 3
Trial 4
Trial 5
Trial 6
Trial 7
Trial 8
Trial 9
Trial 10
Trial 11
Trial 12
Trial 13
Trial 14
Trial 15
Trial 16
Trial 17
Trial 18
Trial 19
Trial 20
Trial 21
Trial 22
Trial 23
Trial 24
Trial 25
Trial 26
Trial 27
Trial 28
Trial 29
Trial 30

Demonstration trial (30x real-time speed) 

Project updates

Natural Robotics Lab: investigating robotic systems inspired by nature, and robotic models of natural systems.

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