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Table 2 Comparing some of the main related works based on their experimental characteristics

From: Multi-Agent Foraging: state-of-the-art and research challenges

   Platform Size and limits of the world Number and nature of food Max number of robots Robot characteristics Energy of the robot Scalability Performance metrics
Computer simulation          
  Hoff et al. (2013) Own developed multi-agent simulator 10 m \(\times \) 10 m continuous One unlimited 20 E-Puck, sensors for nest, food, and obstacles in direct proximity, communicate with nearby robots, measure the range and bearing from which each transmission came. Robots do not have global position measurement or global communication Unlimited N/A Food found or not, How quickly food is found, the rate at which it returned the food to the nest
  Lee et al. (2013) Stage/Player Circle of radius = 80 continuous One food is generated at a random position per ten seconds 40 Agents can store information, sense locally their world and communicate with each other in unlimited range Limited Consider 25, 30, 35 and 40 agents Energy efficiency
  Magdy et al. (2013) Own developed multi-agent simulator N/A One limited at fixed position 50 Agents are Turing machine equivalent which can communicate with nearby robots Unlimited Consider 10, 20, 25, 30, 40 and 50 agents Food found or not at limited time, speed to find food
  Pitonakova et al. (2014) Own developed multi-agent simulator 4000 \(\times \) 4000 continuous-space with periodic boundaries Between 10 and 100 deposits with varied quality 100 Size 10 \(\times \) 10 units, subsumption architecture, initially randomly oriented, carry one unit, odometry, memory to store energy efficiency of a deposits Limited Consider 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100 agents Proportion of collected food
  Simonin et al. (2014) TurtleKit simulation platform 2D bounded grid of varied size 25 \(\times \) 25 cells to 800 \(\times \) 800 cells 20 with 10 units and 20 units each randomly distributed 160 Subsumption architecture, size of one cell, memory less, perceive the four neighboring cells, write on current cell integer value (APF value), read from cell, color trails with specific color, detect and follow trails, one cell can contain multiple agents Unlimited Consider 5, 10, 20, 40, 80 and 160 agents Average foraging time
  Zedadra et al. (2016) Netlogo simulator 2D bounded grid of varied size 100 \(\times \) 100 cells to 1200 \(\times \) 1200 cells 1 to 10 sites with 500 to 1500 units 10,000 Subsumption architecture, size of one cell, memory less, perceive the four neighboring cells, deposit a pheromone on current cell, color trails with specific color, detect and follow trails, one cell can contain multiple agents Unlimited Consider 1 to 10000 agents Average Foraging Time, Total Food Returned, Average Path Length
  Zedadra et al. (2016) Netlogo simulator 2D bounded grid of varied size 100 \(\times \) 100 cells to 1000 \(\times \) 1000 cells 1 to 10 locations, each with 500 units 1000 Subsumption architecture, size of one cell, memory less, perceive the four neighboring cells, deposit a pheromone on current cell, color trails with specific color, detect and follow trails, one cell can contain multiple agents Limited Consider 100 to 1000 agents Total energy consumed, energy efficiency
  Johnson and Brown (2016) Enki 2.0 robot simulator Circularly bounded 2D environment Cylinders with a diameter of 10 cm N/A Enki’s e-puck model which have a diameter of 7.4 cm, inter-wheel distance of 5.1 cm, and weight of 152 g Unlimited N/A Cluster targets to specific location
Real experiments          
  Alers et al. (2014) Turtlebot platform Bounded 2D environment One limited food 4 Turtlebot equipped with a laptop with a core-i3 CPU for computation, Kinect sensor, RGBD information used to detect and locate AR markers, wheel odometry and gyro information, six unique markers, toolkit called ALVAR, wi- with a UDP connection Unlimited N/A Did robots converge to foraging
  Geuther et al. (2012) Lego Mindstorms NXT kits 50x50 grid Energy spots Scouts: 25;Harvesters: 60 USB port and Bluetooth module to communicate with a central system, IR light sources, compass sensor Unlimited Varying scouts 5, 10, 15, 20 and 25; Varying harvesters 20, 30, 40, 50 and 60 Total energy harvested
  Pitonakova et al. (2016) ARGoS simulation environment Continuous space and updates itself 10 times per second, 50 m 50 m N deposit with volume v and quality Q varied at each simulation 25 MarXbot robots, differentially steered with a diameter of 0.17 m, four color sensors pointed to the g round, 2 4 infrared proximity sensors, light sensor used for navigation towards the base, a range and b earing module, wheel-mounted sensors utilized for odometry and a ring of eight color LEDs used for debugging Unlimited N/A Resource collected
  Russell et al. (2015) N/A Continuous real world with 28 pre-deployed beacons One food 8 Differential drive robots of authors own design, capable of grasping a single beacon, moving it from place to place, Arduino Uno micro-controller coupled with a Raspberry Pi Linux computer, outfitted with a camera, 802.11 wireless communication, USB interfaces, five Sharp IR infrared distance sensors, two simple bump sensors, two encoded wheels, an embedded gripper capable of collecting small cans, a flat push surface, and an I2C-driven display, outfitted with a Tmote Sky wireless sensor mote attached to the Raspberry Pi, sensor motes communicated over 802.15.4, channel 26, via UDP multi-cast Unlimited Varying 1, 2, 4, and 8 robots Total food pellets gathered so far
  Heinerman et al. (2016) N/A \(\times \) 1 m arena One target 6 Thymio II robot, seven Infra-Red (IR) proximity sensors for obstacle detection, differential drive with the maximum wheel actuators set between −500 and 500, a cam-era, wireless communication, and a high capacity battery, A WiFi dongle for communication, battery, allowing for a total experimental time of 10 hours, LEGO gripper Limited N/A Number of pucks collected in ten-minute intervals