COMMUNICATION IN MULTIROBOT TEAMS - BARATH CHRISTOPHER PETIT

PAPER COMMUNICATION IN REACTIVE MULTIAGENT SYSTEMS -Tucker Balch and Ronald Arkin

ISSUES ● Multiple robot teams are faster and reliable than a single robot system. ● Can communication between the robots in a multirobot team enhance the performance? ● What level of communication yields the best performance?(relative to the performance metrics being used to evaluate the performance.)

PERFORMANCE EVALUATION ● Three benchmark tasks were devised to evaluate system performance, namely: ● FORAGE ● CONSUME ● GRAZING

PERFORMANCE EVALUATION ● Six parameters for classification ● TASK ● COMMUNICATION TYPE ● NUMBER OF ROBOTS ● NUMBER OF ATTRACTORS ● MASS OF ATTRACTORS ● PERCENTAGE OF OBSTACLE COVERAGE

THE FORAGE TASK ● Robot wanders in environment looking for items of interest (attractors). ● Once attractor is sighted, robot moves towards it, acquires it and finally returns it to specified home base. ● Mass of attractor dictates completion time. ● Several robots can cooperate in carrying an attractor to home base but speed of slowest robot will be the bottleneck.

THE CONSUME TASK ● Similar to FORAGE but after acquiring the attractor the robot operates on the attractor instead of carrying it to a home base. ● Time to completion is proportional to mass of attractor. ● Several robots can cooperate while operating or 'consuming' the attractor. ● Rate of consumption is linear with number of operating robots, (there is no ceiling).

THE GRAZE TASK ● Unlike FORAGE, CONSUME there are no discrete attractors. ● Aim is to completely visit the environment (or some percentage of it). ● Time of completion dictated by size of the environment. ● Multiple robots reduce time if they avoid previously grazed areas and if they can sight ungrazed areas quickly.

TASK PARAMETERS ● NUMBER OF ATTRACTORS (for FORAGE and CONSUME). ● MASS OF ATTRACTORS (for FORAGE and CONSUME). ● GRAZE COVERAGE (for GRAZE).

COMPLEX TASKS Complex tasks can be viewed as being a combination of simpler tasks like foraging, consume or grazing.

Wander Acquire Deliver Encounter Deposit Attach The FORAGE FSA

THE WANDER STATE ● Noise: high gain to maximize coverage. ● Avoid-static-obstacle (for objects): high to avoid collisions. ● Avoid-static-obstacle (for robots) : high to avoid other robots to ensure maximum and efficient area coverage. ● Detect-attractor: a perceptual schema that is triggered when an attractor is sighted, enabling the transition to the ACQUIRE state.

THE ACQUIRE STATE ● Noise: low gain, to overcome local minimas ● Avoid-static-obstacle (for objects): high to avoid collisions with obstacles. ● Avoid-static-obstacle (for robots): very low so that robots can converge on same attractor to cooperate. ● Move-to-goal: high to move to the detected attractor. ● Detect-attachment: a perceptual schema that is triggered when robot is close enough to attach to the attractor.

THE DELIVER STATE ● Noise: low to overcome local minima. ● Avoid-static-obstacle (for objects): high to avoid collisions. ● Avoid-static-obstacle (for robots): low to enable robots to cooperate. ● Move-to-goal: high (target is home base). ● Detect-deposit: a perceptual schema that is triggered when home base is reached.

Wander Acquire Consume Encounter Complete Attach The CONSUME FSA

CONSUME ● WANDER and ACQUIRE states are similar to states in FORAGE but instead of DELIVER, the CONSUME state is used. ● In CONSUME, only one motor schema is active which reduces the mass of the attractor till it becomes zero. ● Once attractor is consumed, the robot transitions to the WANDER state.

Wander Acquire Graze Encounter Encounter grazed area Move to ungrazed part The GRAZE FSA

The GRAZE state ● Noise: low gain to overcome local minima. ● Avoid-static-obstacle: (for objects) high to avoid collision. ● Avoid-static-obstacle: (for robots) very low to enable robots to graze closeby. ● Probe: moderate to enable robot to move along its current heading. ● Graze: the graze mechanism, which 'tags' the area being grazed. ● Detect-grazed-area: this triggers transition to WANDER.

INTER–AGENT COMMUNICATION ● NO COMMUNICATION ● STATE COMMUNICATION ● GOAL COMMUNICATION

NO COMMUNICATION ● Robots can sense other robots, obstacles and attractors. ● However none of the information is transmitted to other robots. ● Note that robots can still cooperate (in tasks like foraging and consume), as was shown by Arkin.

STATE COMMUNICATION ● Robots can detect internal states of other robots. ● Single bit communication: a one indicates that the robot is in WANDER state, and a zero indicates a state other than WANDER. ● State communication need not be deliberate.

GOAL COMMUNICATION ● Transmission and reception of goal information. ● Needs to be deliberate, unlike previous two modes of communication. ● Robot can get to goal directly rather than follow the sender robot (unlike state communication).

PERFORMANCE METRICS ● COST: minimize system cost. Aims to reduce number of robots. ● TIME: minimizes completion time. Tends to increase number of robots. ● ENERGY: minimizes the energy expended in completing the task. ● RELIABILITY/SURVIVABILITY: Priority is on completion of task.

BASELINE PERFORMANCE ● 3-dimensional plot ● ● Maximum time for single robot. ● Minimum time for maximum robots. ● In some cases, improvement in time with addition of robots is not significant. ● For given number of robots, it takes longer to complete the task with more number of attractors.

AMORTIZED COST METRIC ● If systems needs to be both fast and inexpensive, then thers is a tradeoff. ● In this case the metric is: N*300 + T. ● N: number of robots. ● T: Time taken. ● The cost of each robot per run is taken as (for eg.) 300. ● For FORAGE, a system with 2 robots is best for 3-4 attractors.

SPEEDUP ● Measures efficiency of n robot team relative to a single robot system in performing a task. ● If attractors are fixed, then it is defined as : (time taken by 1 robot)/ (n * time taken by n robots). ● Speedup is higher for larger number of attractors. ● Speedup is sublinear for CONSUME, but can be superlinear for lower mass of attractors. ● Speedup for GRAZE is mostly superlinear.

RESULTS WITH COMMUNICATION ● FORAGE:State communication improved performance by 16%. Goal communication is better than state communication by 3%. ● CONSUME: State communication gives 10% improvement. Goal communication gives 6% improvement (over no communication). ● For low mass attractors, Goal communication almost indistinguishable from state communication. ● GRAZE: Communication has hardly any effect, due to implicit communication.

RESULTS ● Initial testing on mobile robots support simulation results. ● Comunication improves performance in tasks with little implicit communication. ● Communication is not effective in tasks which include implicit communication. ● More complex strategies offer little or no benefit over low-level communication.