Renyi Entropy based Target Tracking in Mobile Sensor Networks

This paper proposes an entropy based target tracking approach for mobile sensor networks. The proposed tracking algorithm runs a target state estimation stage and a motion control stage alternatively. A distributed particle filter is developed to estimate the target position in the first stage. This distributed particle filter does not require to transmit the weighted particles from one sensor node to another. Instead, a Gaussian mixture model is formulated to approximate the posterior distribution represented by the weighted particles via an EM algorithm. The EM algorithm is developed in a distributed form to compute the parameters of Gaussian mixture model via local communication, which leads to the distributed implementation of the particle filter. A flocking controller is developed to control the mobile sensor nodes to track the target in the second stage. The flocking control algorithm includes three components. Collision avoidance component is based on the design of a separation potential function. Alignment component is based on a consensus algorithm. Navigation component is based on the minimization of an quadratic Renyi entropy. The quadratic Renyi entropy of Gaussian mixture model has an analytical expression so that its optimization is feasible in mobile sensor networks. The proposed active tracking algorithm is tested in simulation. © 2011 IFAC

A modified model for the Lobula Giant Movement Detector and its FPGA implementation

The Lobula Giant Movement Detector (LGMD) is a wide-field visual neuron located in the Lobula layer of the Locust nervous system. The LGMD increases its firing rate in response to both the velocity of an approaching object and the proximity of this object. It has been found that it can respond to looming stimuli very quickly and trigger avoidance reactions. It has been successfully applied in visual collision avoidance systems for vehicles and robots. This paper introduces a modified neural model for LGMD that provides additional depth direction information for the movement. The proposed model retains the simplicity of the previous model by adding only a few new cells. It has been simplified and implemented on a Field Programmable Gate Array (FPGA), taking advantage of the inherent parallelism exhibited by the LGMD, and tested on real-time video streams. Experimental results demonstrate the effectiveness as a fast motion detector

LiDAR and Camera Detection Fusion in a Real Time Industrial Multi-Sensor Collision Avoidance System

Collision avoidance is a critical task in many applications, such as ADAS (advanced driver-assistance systems), industrial automation and robotics. In an industrial automation setting, certain areas should be off limits to an automated vehicle for protection of people and high-valued assets. These areas can be quarantined by mapping (e.g., GPS) or via beacons that delineate a no-entry area. We propose a delineation method where the industrial vehicle utilizes a LiDAR {(Light Detection and Ranging)} and a single color camera to detect passive beacons and model-predictive control to stop the vehicle from entering a restricted space. The beacons are standard orange traffic cones with a highly reflective vertical pole attached. The LiDAR can readily detect these beacons, but suffers from false positives due to other reflective surfaces such as worker safety vests. Herein, we put forth a method for reducing false positive detection from the LiDAR by projecting the beacons in the camera imagery via a deep learning method and validating the detection using a neural network-learned projection from the camera to the LiDAR space. Experimental data collected at Mississippi State University's Center for Advanced Vehicular Systems (CAVS) shows the effectiveness of the proposed system in keeping the true detection while mitigating false positives.Comment: 34 page

Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search

Target search with unmanned aerial vehicles (UAVs) is relevant problem to many scenarios, e.g., search and rescue (SaR). However, a key challenge is planning paths for maximal search efficiency given flight time constraints. To address this, we propose the Obstacle-aware Adaptive Informative Path Planning (OA-IPP) algorithm for target search in cluttered environments using UAVs. Our approach leverages a layered planning strategy using a Gaussian Process (GP)-based model of target occupancy to generate informative paths in continuous 3D space. Within this framework, we introduce an adaptive replanning scheme which allows us to trade off between information gain, field coverage, sensor performance, and collision avoidance for efficient target detection. Extensive simulations show that our OA-IPP method performs better than state-of-the-art planners, and we demonstrate its application in a realistic urban SaR scenario.Comment: Paper accepted for International Conference on Robotics and Automation (ICRA-2019) to be held at Montreal, Canad

Bounded Distributed Flocking Control of Nonholonomic Mobile Robots

There have been numerous studies on the problem of flocking control for multiagent systems whose simplified models are presented in terms of point-mass elements. Meanwhile, full dynamic models pose some challenging problems in addressing the flocking control problem of mobile robots due to their nonholonomic dynamic properties. Taking practical constraints into consideration, we propose a novel approach to distributed flocking control of nonholonomic mobile robots by bounded feedback. The flocking control objectives consist of velocity consensus, collision avoidance, and cohesion maintenance among mobile robots. A flocking control protocol which is based on the information of neighbor mobile robots is constructed. The theoretical analysis is conducted with the help of a Lyapunov-like function and graph theory. Simulation results are shown to demonstrate the efficacy of the proposed distributed flocking control scheme

Nonlinear Model Predictive Control for Multi-Micro Aerial Vehicle Robust Collision Avoidance

Multiple multirotor Micro Aerial Vehicles sharing the same airspace require a reliable and robust collision avoidance technique. In this paper we address the problem of multi-MAV reactive collision avoidance. A model-based controller is employed to achieve simultaneously reference trajectory tracking and collision avoidance. Moreover, we also account for the uncertainty of the state estimator and the other agents position and velocity uncertainties to achieve a higher degree of robustness. The proposed approach is decentralized, does not require collision-free reference trajectory and accounts for the full MAV dynamics. We validated our approach in simulation and experimentally.Comment: Video available on: https://www.youtube.com/watch?v=Ot76i9p2ZZo&t=40

A unified approach to cooperative and non-cooperative sense-and-avoid

Cooperative and non-cooperative Sense-and-Avoid (SAA) capabilities are key enablers for Unmanned Aircraft Vehicle (UAV) to safely and routinely access all classes of airspace. In this paper state-of-the-art cooperative and non-cooperative SAA sensor/system technologies for small-to-medium size UAV are identified and the associated multi-sensor data fusion techniques are introduced. A reference SAA system architecture is presented based on Boolean Decision Logics (BDL) for selecting and sorting non-cooperative and cooperative sensors/systems including both passive and active Forward Looking Sensors (FLS), Traffic Collision Avoidance System (TCAS) and Automatic Dependent Surveillance - Broadcast (ADS-B). After elaborating the SAA system processes, the key mathematical models associated with both non-cooperative and cooperative SAA functions are presented. The analytical models adopted to compute the overall uncertainty volume in the airspace surrounding an intruder are described. Based on these mathematical models, the SAA Unified Method (SUM) for cooperative and non-cooperative SAA is presented. In this unified approach, navigation and tracking errors affecting the measurements are considered and translated to unified range and bearing uncertainty descriptors, which apply both to cooperative and non-cooperative scenarios. Simulation case studies are carried out to evaluate the performance of the proposed SAA approach on a representative host platform (AEROSONDE UAV) and various intruder platforms. Results corroborate the validity of the proposed approach and demonstrate the impact of SUM towards providing a cohesive logical framework for the development of an airworthy SAA capability, which provides a pathway for manned/unmanned aircraft coexistence in all classes of airspace