Cooperative object transport with a swarm of e-puck robots: robustness and scalability of evolved collective strategies

Cooperative object transport in distributed multi-robot systems requires the coordination and synchronisation of pushing/pulling forces by a group of autonomous robots in order to transport items that cannot be transported by a single agent. The results of this study show that fairly robust and scalable collective transport strategies can be generated by robots equipped with a relatively simple sensory apparatus (i.e. no force sensors and no devices for direct communication). In the experiments described in this paper, homogeneous groups of physical e-puck robots are required to coordinate and synchronise their actions in order to transport a heavy rectangular cuboid object as far as possible from its starting position to an arbitrary direction. The robots are controlled by dynamic neural networks synthesised using evolutionary computation techniques. The best evolved controller demonstrates an effective group transport strategy that is robust to variability in the physical characteristics of the object (i.e. object mass and size of the longest object’s side) and scalable to different group sizes. To run these experiments, we designed, built, and mounted on the robots a new sensor that returns the agents’ displacement on a 2D plane. The study shows that the feedback generated by the robots’ sensors relative to the object’s movement is sufficient to allow the robots to coordinate their efforts and to sustain the transports for an extended period of time. By extensively analysing successful behavioural strategies, we illustrate the nature of the operational mechanisms underpinning the coordination and synchronisation of actions during group transport

Cooperative object transport with a swarm of e-puck robots: robustness and scalability of evolved collective strategies

Cooperative object transport in distributed multi-robot systems requires the coordination and synchronisation of pushing/pulling forces by a group of autonomous robots in order to transport items that cannot be transported by a single agent. The results of this study show that fairly robust and scalable collective transport strategies can be generated by robots equipped with a relatively simple sensory apparatus (i.e. no force sensors and no devices for direct communication). In the experiments described in this paper, homogeneous groups of physical e-puck robots are required to coordinate and synchronise their actions in order to transport a heavy rectangular cuboid object as far as possible from its starting position to an arbitrary direction. The robots are controlled by dynamic neural networks synthesised using evolutionary computation techniques. The best evolved controller demonstrates an effective group transport strategy that is robust to variability in the physical characteristics of the object (i.e. object mass and size of the longest object’s side) and scalable to different group sizes. To run these experiments, we designed, built, and mounted on the robots a new sensor that returns the agents’ displacement on a 2D plane. The study shows that the feedback generated by the robots’ sensors relative to the object’s movement is sufficient to allow the robots to coordinate their efforts and to sustain the transports for an extended period of time. By extensively analysing successful behavioural strategies, we illustrate the nature of the operational mechanisms underpinning the coordination and synchronisation of actions during group transport

Cooperative transport in swarm robotics. Multi object transportation

Swarm robotics is a research field inspired from the natural behavior of ants, bees or fish in their natural habitat. Each group display swarm behavior in different ways. For example, ants use pheromones to trace one another in order to find a nest, reach a food source or do any operation, while bees use dance moves to attract one another to the desired place. In swarm robotics, small robots attempt to mimic insect behavior. The robotic swarm group collaborate to perform a task and collectively solve a given problem. In the process, the robots use the sensors they are equipped with to move, communicate or avoid obstacles until they collectively do the desired functionality. In this thesis, we propose a modification to the Robotic Darwinian Particle Swarm Optimization (RDPSO) algorithm. In the RDPSO, robots deployed in a rescue operation, transport one object at a time to a desired safe place. In our algorithm, we simultaneously transport multiple objects to safety. We call our algorithm Multi Robotics Darwinian Particle Swarm Optimization (MRDPSO). Our algorithm is developed and implemented on a VREP simulator using ePuck robots as swarm members. We test our algorithm using two different environment sizes complete with obstacles. First implementation is for two simultaneous object transported but can be extended to more than two. We compare our new algorithm to the results of single RDPSO and found our algorithm to be 35 to 41 % faster. We also compared our results to those obtained from three selected papers that are Ghosh, Konar, and Janarthanan [1], TORABI [2], and Kube and Bonabeau [3]. The performance measures we compare to are the accuracy of transporting all objects to desired location, and the time efficiency of transporting all the objects in our new system

Modular Hydraulic Propulsion: A Robot that Moves by Routing Fluid Through Itself

This paper introduces the concept of Modular Hydraulic Propulsion, in which a modular robot that operates in a fluid environment moves by routing the fluid through itself. The robot’s modules represent sections of a hydraulics network. Each module can move fluid between any of its faces. The modules (network sections) can be rearranged into arbitrary topologies. We propose a decentralized motion controller, which does not require modules to communicate, compute, nor store information during run-time. We use 3-D simulations to compare the performance of this controller to that of a centralized controller with full knowledge of the task. We also detail the design and fabrication of six 2-D prototype modules, which float in a water tank. Results of systematic experiments show that the decentralized controller, despite its simplicity, reliably steers modular robots towards a light source. Modular Hydraulic Propulsion could offer new solutions to problems requiring reconfigurable systems to move precisely in 3-D, such as inspection of pipes, vascular systems or other confined spaces

Goal Based Human Swarm Interaction for Collaborative Transport

Human-swarm interaction is an important milestone for the introduction of swarm-intelligence based solutions into real application scenarios. One of the main hurdles towards this goal is the creation of suitable interfaces for humans to convey the correct intent to multiple robots. As the size of the swarm increases, the complexity of dealing with explicit commands for individual robots becomes intractable. This brings a great challenge for the developer or the operator to drive robots to finish even the most basic tasks. In our work, we consider a different approach that humans specify only the desired goal rather than issuing individual commands necessary to obtain this task. We explore this approach in a collaborative transport scenario, where the user chooses the target position of an object, and a group of robots moves it by adapting themselves to the environment. The main outcome of this thesis is the design of integration of a collaborative transport behavior of swarm robots and an augmented reality human interface. We implemented an augmented reality (AR) application in which a virtual object is displayed overlapped on a detected target object. Users can manipulate the virtual object to generate the goal configuration for the object. The designed centralized controller translate the goal position to the robots and synchronize the state transitions. The whole system is tested on Khepera IV robots through the integration of Vicon system and ARGoS simulator