Particle Swarm Optimization Based Source Seeking

Signal source seeking using autonomous vehicles is a complex problem. The complexity increases manifold when signal intensities captured by physical sensors onboard are noisy and unreliable. Added to the fact that signal strength decays with distance, noisy environments make it extremely difficult to describe and model a decay function. This paper addresses our work with seeking maximum signal strength in a continuous electromagnetic signal source with mobile robots, using Particle Swarm Optimization (PSO). A one to one correspondence with swarm members in a PSO and physical Mobile robots is established and the positions of the robots are iteratively updated as the PSO algorithm proceeds forward. Since physical robots are responsive to swarm position updates, modifications were required to implement the interaction between real robots and the PSO algorithm. The development of modifications necessary to implement PSO on mobile robots, and strategies to adapt to real life environments such as obstacles and collision objects are presented in this paper. Our findings are also validated using experimental testbeds.Comment: 13 pages, 12 figure

Standard Particle Swarm Optimization on Source Seeking Using Mobile Robots

In this paper, we explore the implementation of standard particle swarm optimization (SPSO) on a swarm of physical mobile robots conducting a source seeking task. The signal source is electromagnetic, whose strength is non-differentiable at many points making most gradient based source seeking strategies ineffective in this scenario. We analyze the physical limitations of the robots and modify SPSO accordingly to make them compatible with each other. We also compare different SPSO topology models to determine the one best suited for our problem. Finally, we incorporate obstacle avoidance strategies into PSO, and compare the performance of original PSO, SPSO 2006 and SPSO 2011 in a complex environment with obstacles. Simulation results demonstrate the efficacy of implementing SPSO to robot source seeking problem. Moreover, it is shown that SPSO 2011 is not only superior as an optimization method, but also provides better performance in robotic implementation compared to SPSO 2006 and original PSO

Communicating Multi-UAV System for Cooperative SLAM-based Exploration

International audienceIn the context of multi-robot system and more generally for Technological System-of-Systems, this paper proposes a multi-UAV (Unmanned Aerial Vehicle) framework for SLAM-based cooperative exploration under limited communication bandwidth. The exploration strategy, based on RGB-D grid mapping and group leader decision making, uses a new utility function that takes into account each robot distance in the group from the unexplored set of targets, and allows to simultaneously explore the environment and to get a detailed grid map of specific areas in an optimized manner. Compared to state-of-the-art approaches, the main novelty is to exchange only the frontier points of the computed local grid map to reduce the shared data volume, and consequently the memory consumption. Moreover, communications constraints are taken into account within a SLAM-based multi-robot collective exploration. In that way, the proposed strategy is also designed to cope with communications drop-out or failures. The multi-UAV system is implemented into ROS and GAZEBO simulators on multiple computers provided with network facilities. Results show that the proposed cooperative exploration strategy minimizes the global exploration time by 25% for 2 UAVs and by 30% for 3 UAVs, while outperforming state-of-the-art exploration strategies based on both random and closest frontiers, and minimizing the average travelled distance by each UAV by 55% for 2 UAVs and by 62% for 3 UAVs. Furthermore, the system performance is also evaluated in a realistic test-bed comprising an infrastructure less network, which is used to support limited communications. The results of the test-bed show that the proposed exploration strategy uses 10 times less data than a strategy that makes the robots exchanging their whole local maps

Hessian Estimation Based Adaptive and Cooperative Extremum Localization

The thesis is on Hessian estimation based adaptive and cooperative extremum localization via a single mobile sensory agent as well as a network of multiple such agents. First, we develop a continuous time adaptive extremum localization of an arbitrary quadratic function F(·) based on Hessian estimation, using the measured signal intensity via a single mobile sensory agent. A gradient based adaptive Hessian parameter estimation and extremum localization scheme is developed considering a linear parametric model of field variations. Next, we extend the proposed single agent based Hessian estimation and extremum localization scheme to consensus based cooperative distributed scheme to be implemented by a network of such sensory agents.For the networked multi-agent case, a consensus term is added to the base adaptive laws to obtain enhanced estimation cooperatively. Stability and convergence analysis of the proposed scheme is studied, establishing asymptotic convergence of the Hessian parameters and location estimates to their true values robustly, provided that the motion of agent(s) satisfies certain persistence of excitation(PE) conditions. Furthermore, we show that for a network of connected agents, the PE requirements can be distributed to the agents so that the requirement on each agent is more relaxed and feasible. Later, we design an adaptive motion control scheme for steering a mobile sensory agent in 2D toward the source of a signal field F(·) using the signal intensity the agent continuously measures at its current location. The proposed adaptive control design is based on the Hessian estimation based adaptive extremum localization. Results are displayed to verify that the proposed scheme is stable, provides asymptotic convergence of the Hessian parameter and extremum location estimates to their true values and the agent location to the source location, robustly to signal measurement noises

Mobile robotic network deployment for intruder detection and tracking

This thesis investigates the problem of intruder detection and tracking using mobile robotic networks. In the first part of the thesis, we consider the problem of seeking an electromagnetic source using a team of robots that measure the local intensity of the emitted signal. We propose a planner for a team of robots based on Particle Swarm Optimization (PSO) which is a population based stochastic optimization technique. An equivalence is established between particles generated in the traditional PSO technique, and the mobile agents in the swarm. Since the positions of the robots are updated using the PSO algorithm, modifications are required to implement the PSO algorithm on real robots to incorporate collision avoidance strategies. The modifications necessary to implement PSO on mobile robots, and strategies to adapt to real environments are presented in this thesis. Our results are also validated on an experimental testbed. In the second part, we present a game theoretic framework for visibility-based target tracking in multi-robot teams. A team of observers (pursuers) and a team of targets (evaders) are present in an environment with obstacles. The objective of the team of observers is to track the team of targets for the maximum possible time. While the objective of the team of targets is to escape (break line-of-sight) in the minimum time. We decompose the problem into two layers. At the upper level, each pursuer is allocated to an evader through a minimum cost allocation strategy based on the risk of each evader, thereby, decomposing the agents into multiple single pursuer-single evader pairs. Two decentralized allocation strategies are proposed and implemented in this thesis. At the lower level, each pursuer computes its strategy based on the results of the single pursuer-single evader target-tracking problem. We initially address this problem in an environment containing a semi-infinite obstacle with one corner. The pursuer\u27s optimal tracking strategy is obtained regardless of the evader\u27s strategy using techniques from optimal control theory and differential games. Next, we extend the result to an environment containing multiple polygonal obstacles. We construct a pursuit field to provide a guiding vector for the pursuer which is a weighted sum of several component vectors. The performance of different combinations of component vectors is investigated. Finally, we extend our work to address the case when the obstacles are not polygonal, and the observers have constraints in motion