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We present a technique that uniformly controls a team of autonomous sensor platforms charged with the dual task of searching for and then tracking a moving target within a recursive Bayesian estima...

This paper presents a technique and its system that localizes a rotary-wing Micro Air Vehicle (MAV) in indoor environments even under Non-Line-of-Sight (NLoS) conditions. The technique is based on the system with the MAV having a wireless microphone and a ground station having multiple microphones. The technique identifies the location of the MAV in two-dimensional space by measuring the Time-of-Arrival (ToA) of acoustic signals caused by the rotation of blades at the MAV. The technique then applies an iterative algorithm that corrects the NLoS errors and computes the true ToA based on maps of known indoor environments in a computationally inexpensive manner. To suppress noise in the received signals and thus improve the robustness of ToA estimation, the generalized cross-correlation with phase transform and a bandpass filter is derived and applied. The performance of the proposed technique was examined experimentally by its comparison with three existing ToA-based localization techniques. Experimental results show the superiority of the proposed technique, demonstrating its potential for aiding indoor MAVs in autonomous navigation.

This paper presents Micro Aerial Vehicles (MAVs) and their cooperative systems including Unmanned Ground Vehicles (UGVs) and a Base Station (BS), which were primarily designed for the 1st US-Asian Demonstration and Assessment on Micro-Aerial and Unmanned Ground Vehicle Technology (MAV08). The MAVs are of coaxial design, which imparts mechanical stability both outdoor and indoor while obeying a 30 cm size constraint. They have carbon fibre frames for weight reduction allowing microcontrollers and various sensors to be mounted on-board for tele-operated and waypoint control. The UGVs are similarly equipped to perform their own search and tracking mission but also to support the MAVs by relaying data between the MAVs and the BS when they are out of direct range. The BS monitors the vehicles and their environment and navigates them autonomously or with humans in the loop through the developed GUI. The ability of the MAV in flight was demonstrated by showing continuous hovering. The efficacy of the overall system was validated by autonomously controlling two UGVs for cooperative search.

PurposeThe purpose of this paper is to present a localisation system for an indoor rotary‐wing micro aerial vehicle (MAV) that uses three onboard LEDs and base station mounted active vision unit.Design/methodology/approachA pair of blade mounted cyan LEDs and a tail mounted red LED are used as on‐board landmarks. A base station tracks the landmarks and estimates the pose of the MAV in real time by analysing images taken using an active vision unit. In each image, the ellipse formed by the cyan LEDs is used for 5 degree of freedom (DoF) pose estimation with yaw estimation from the red LED providing the 6th DoF.FindingsAbout 1‐3.5 per cent localisation error of the MAV at various ranges, rolls and angular speeds less than 45°/s relative to the base station at known location indicates that the MAV can be accurately localised at 9‐12 Hz in an indoor environment.Research limitations/implicationsLine‐of‐sight between the base station and MAV is necessary while limited accuracy is evident in yaw estimation at long distances. Additional yaw sensors and dynamic zoom are among future work.Practical implicationsProvided an unmanned ground vehicle (UGV) as the base station equipped with its own localisation sensor, the developed system encourages the use of autonomous indoor rotary‐wing MAVs in various robotics applications, such as urban search and rescue.Originality/valueThe most significant contribution of this paper is the innovative LED configuration allowing full 6 DoF pose estimation using three LEDs, one camera and no fixed infrastructure. The active vision unit enables a wide range of observable flight as the ellipse generated by the cyan LEDs is recognisable from almost any direction.

This paper presents a non-line-of-sight (NLOS) localization technique and its system for a quiet and known environment that uses first-arrival low-frequency acoustic signals. The proposed technique first measures the time-of-arrivals (ToAs) of the first-arrival low-frequency sounds. The technique then estimates the location of a target by minimizing the differences between the measured ToAs and the corresponding ToAs estimated from the known map. The strength of the proposed technique is that the positioning accuracy is not corrupted by any small or known obstacle. The performance of the technique was investigated by its comparison to other techniques in both simulations and an experiment. The numerical results show successful localization of a target in various situations, demonstrating the efficacy of the technique and system as a feasible solution for NLOS mitigation.

This paper presents a new acoustic approach to locate a mobile target outside the field-of-view (FOV), or the non-field-of-view (NFOV) of an optical sensor, based on the reflection and diffraction signals. In this approach, a sensor platform determines a reflection time-difference-of-arrival (TDOA) and frequency dependent diffraction to two distinct observation likelihoods from a single target's sound. The fusion of these likelihoods, a joint acoustic observation likelihood, estimate the NFOV target probabilistically within the recursive Bayesian estimation (RBE) framework. The approach was formulated and derived mathematically. Through parametric studies in simulation, the potential of the proposed approach for practical implementation has been demonstrated by the successful localization of the sound source. Finally, a preliminary validation of sound separation was performed in a controlled experimental environment showing the difference between diffraction alone and combination of diffraction and reflection signals.

This paper presents a platform- and hardware-in-the-loop simulator (PHILS), which was developed for the evaluation of cooperative performance of multiple autonomous robots and their testing in virtual environments. The simulator consists of computers each with a graphics processing unit (GPU), monitors, a network switch that links the computers, and a server-client simulation software system installed on the computers. The cooperative performance of multiple autonomous robots can be evaluated by linking computers each to be mounted on a robot to the network and running and testing autonomous robots in a virtual environment. Unlike the conventional hardware-in-the-loop simulators or multi-robot simulators, the primary advantage of the PHILS is that it can test cooperative autonomous robots and analyze their cooperative performance as well as hardware performance in a real-time virtual environment, enabling the implementation of synchronous and asynchronous communication strategies and the control of communication delay and loss.