Swarm robotics: Cooperative navigation in unknown environments

Swarm Robotics is garnering attention in the robotics field due to its substantial benefits. It has been proven to outperform most other robotic approaches in many applications such as military, space exploration and disaster search and rescue missions. It is inspired by the behavior of swarms of social insects such as ants and bees. It consists of a number of robots with limited capabilities and restricted local sensing. When deployed, individual robots behave according to local sensing until the emergence of a global behavior where they, as a swarm, can accomplish missions individuals cannot. In this research, we propose a novel exploration and navigation method based on a combination of Probabilistic Finite Sate Machine (PFSM), Robotic Darwinian Particle Swarm Optimization (RDPSO) and Depth First Search (DFS). We use V-REP Simulator to test our approach. We are also implementing our own cost effective swarm robot platform, AntBOT, as a proof of concept for future experimentation. We prove that our proposed method will yield excellent navigation solution in optimal time when compared to methods using either PFSM only or RDPSO only. In fact, our method is proved to produce 40% more success rate along with an exploration speed of 1.4x other methods. After exploration, robots can navigate the environment forming a Mobile Ad-hoc Network (MANET) and using the graph of robots as network nodes

Solving the Communication Channel Association Problem for Mobile Robots

Robots working in teams can benefit from recruiting the help of conveniently located nearby robots. To do this an initiating robot needs to be aware of the network addresses of its neighbors. However, a robot is typically aware of its neighbors' relative positions through locally sensed information, such as range and bearing, which does not include the network ID of the neighbor. In this work, robots use a simple visual gesture, such as a light being turned on or off, paired with wireless messages to rapidly and effectively establish a one-to-one association between the relative positions (visual IDs) of neighboring robots and their network addresses (wireless IDs). We identify and formalize the problem of associating the two types of IDs – the association problem, and explore its structure in detail. We also identify that the visual gesture along with the sensor which detects it form a second communication channel that is used by the robots to transmit information. We present two deterministic and one probabilistic algorithm which solve the association problem for stationary robots. Furthermore, we introduce modifications to the probabilistic algorithm in order to tackle the harder, mobile robot version of the problem where robot motion results in changing connectivity between robots. Our algorithmic approach exploits the physically situated properties of the visual IDs to help solve the association problem. We identify key parameters, such as robot density, communication range and movement speed, and study their effect on the performance of the probabilistic algorithm. We use a population growth modeling framework, called Branching Processes, as part of a set of models for the association process which can be used to predict the macroscopic performance of a multi-robot system running the probabilistic algorithms. This set of models can be used to determine how successful the probabilistic algorithm is at solving the association problem in any multi-robot system based on the above mentioned key parameters. The framework can also be used to fine-tune parameters when designing a system so that its performance achieves some desired threshold

Implementing Cooperative Behavior & Control Using Open Source Technology Across Heterogeneous Vehicles

This thesis describes the research effort into implementing cooperative behavior and control across heterogeneous vehicles using low cost off-the-shelf technologies and open source software. Current cooperative behavior and control methods are explored and improved upon to build analysis models. These analysis models characterize ideal factor settings for implementation and establish limits of performance for these low cost approaches to cooperative behavior and control. The research focused on latency and position accuracy as the two measures of performance. Three different ground control station (GCS) software applications and two types of vehicles, rover ground vehicles and aerial multi-rotors, were used in this research. Using optimum factor settings from Design of Experiments (DOE), the multi-rotor following rover vehicle configuration experienced almost twice the latency of other experiments but also the lowest positional error of 0.8 m. Results show that the achieved update frequency of 0.5 Hz or slower would be far too slow for close-formation flight

Spatially targeted communication and self-assembly

We introduce spatially targeted communication - a communication method for multirobot systems. This method allows an individual message sending robot to isolate selected message recipient robots based on their spatial location. The recipient robots can then be sent information targeted solely at them, even if the sending robot uses a broadcast communication modality. We demonstrate spatially targeted communication using a heterogeneous multirobot system composed of flying robots and ground-based self-assembling robots. Flying robots use their privileged view of the environment to determine and communicate information to groups of ground-based robots on what morphologies to form to carry out upcoming tasks. © 2012 IEEE.SCOPUS: cp.pinfo:eu-repo/semantics/publishe

Spatially Targeted Communication and Self-assembly. Video proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems

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