442 research outputs found
Methods for Online UAV Path Planning for Tracking Multiple Objects
Unmanned aerial vehicles (UAVs) or drones have rapidly evolved to enable carrying various sensors such as thermal sensors for vision or antennas for radio waves. Therefore, drones can be transformative for applications such as surveillance and monitoring because they have the capability to greatly reduce the time and cost associated with traditional tasking methods. Realising this potential necessitates equipping UAVs with the ability to perform missions autonomously. This dissertation considers the problems of online path planning for UAVs for the fundamental task of surveillance comprising of tracking and discovering multiple mobile objects in a scene. Tracking and discovering an unknown and time-varying number of objects is a challenging problem in itself. Objects such as people or wildlife tend to switch between various modes of movements. Measurements received by the UAV’s on-board sensors are often very noisy. In practice, the on-board sensors have a limited field of view (FoV), hence, the UAV needs to move within range of the mobile objects that are scattered throughout a scene. This is extremely challenging because neither the exact number nor locations of the objects of interest are available to the UAV. Planning the path for UAVs to effectively detect and track multi-objects in such environments poses additional challenges. Path planning techniques for tracking a single object are not applicable. Since there are multiple moving objects appearing and disappearing in the region, following only certain objects to localise them accurately implies that a UAV is likely to miss many other objects. Furthermore, online path planning for multi-UAVs remains challenging due to the exponential complexity of multi-agent coordination problems. In this dissertation, we consider the problem of online path planning for UAV-based localisation and tracking of multi-objects. First, we realised a low cost on-board radio receiver system on aUAV and demonstrated the capability of the drone-based platform for autonomously tracking and locating multiple mobile radio-tagged objects in field trials. Second, we devised a track-before-detect filter coupled with an online path planning algorithm for joint detection and tracking of radio-tagged objects to achieve better performance in noisy environments. Third, we developed a multi-objective planning algorithm for multi-agents to track and search multi-objects under the practical constraint of detection range limited on-board sensors (or FoV limited sensors). Our formulation leads to a multi-objective value function that is a monotone submodular set function. Consequently, it allows us to employ a greedy algorithm for effectively controlling multi-agents with a performance guarantee for tracking discovered objects while searching for undiscovered mobile objects under practical constraints of limited FoV sensors. Fourth, we devised a fast distributed tracking algorithm that can effectively track multi-objects for a network of stationary agents with different FoVs. This is the first such solution to this problem. The proposed method can significantly improve capabilities of a network of agents to track a large number of objects moving in and out of the limited FoV of the agents’ sensors compared to existing methods that do not consider the problem of unknown and limited FoV of sensors.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202
LAVAPilot: Lightweight UAV Trajectory Planner with Situational Awareness for Embedded Autonomy to Track and Locate Radio-tags
Tracking and locating radio-tagged wildlife is a labor-intensive and
time-consuming task necessary in wildlife conservation. In this article, we
focus on the problem of achieving embedded autonomy for a resource-limited
aerial robot for the task capable of avoiding undesirable disturbances to
wildlife. We employ a lightweight sensor system capable of simultaneous (noisy)
measurements of radio signal strength information from multiple tags for
estimating object locations. We formulate a new lightweight task-based
trajectory planning method-LAVAPilot-with a greedy evaluation strategy and a
void functional formulation to achieve situational awareness to maintain a safe
distance from objects of interest. Conceptually, we embed our intuition of
moving closer to reduce the uncertainty of measurements into LAVAPilot instead
of employing a computationally intensive information gain based planning
strategy. We employ LAVAPilot and the sensor to build a lightweight aerial
robot platform with fully embedded autonomy for jointly tracking and planning
to track and locate multiple VHF radio collar tags used by conservation
biologists. Using extensive Monte Carlo simulation-based experiments,
implementations on a single board compute module, and field experiments using
an aerial robot platform with multiple VHF radio collar tags, we evaluate our
joint planning and tracking algorithms. Further, we compare our method with
other information-based planning methods with and without situational awareness
to demonstrate the effectiveness of our robot executing LAVAPilot. Our
experiments demonstrate that LAVAPilot significantly reduces (by 98.5%) the
computational cost of planning to enable real-time planning decisions whilst
achieving similar localization accuracy of objects compared to information gain
based planning methods, albeit taking a slightly longer time to complete a
mission.Comment: Accepted to 2020 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS
ConservationBots: Autonomous Aerial Robot for Fast Robust Wildlife Tracking in Complex Terrains
Today, the most widespread, widely applicable technology for gathering data
relies on experienced scientists armed with handheld radio telemetry equipment
to locate low-power radio transmitters attached to wildlife from the ground.
Although aerial robots can transform labor-intensive conservation tasks, the
realization of autonomous systems for tackling task complexities under
real-world conditions remains a challenge. We developed ConservationBots-small
aerial robots for tracking multiple, dynamic, radio-tagged wildlife. The aerial
robot achieves robust localization performance and fast task completion times
-- significant for energy-limited aerial systems while avoiding close
encounters with potential, counter-productive disturbances to wildlife. Our
approach overcomes the technical and practical problems posed by combining a
lightweight sensor with new concepts: i) planning to determine both trajectory
and measurement actions guided by an information-theoretic objective, which
allows the robot to strategically select near-instantaneous range-only
measurements to achieve faster localization, and time-consuming sensor rotation
actions to acquire bearing measurements and achieve robust tracking
performance; ii) a bearing detector more robust to noise and iii) a tracking
algorithm formulation robust to missed and false detections experienced in
real-world conditions. We conducted extensive studies: simulations built upon
complex signal propagation over high-resolution elevation data on diverse
geographical terrains; field testing; studies with wombats (Lasiorhinus
latifrons; nocturnal, vulnerable species dwelling in underground warrens) and
tracking comparisons with a highly experienced biologist to validate the
effectiveness of our aerial robot and demonstrate the significant advantages
over the manual method.Comment: 33 pages, 21 figure
Online 3D path planning for Tri-copter drone using GWO-IBA algorithm
Robots at present are involved in many parts of life, especially mobile robots, which are two parts, ground robots and flying robots, and the best example of a flying robot is the drone. Path planning is a fundamental part of UAVs because the drone follows the path that leads it to goal with obstacle avoidance. Therefore, this paper proposes a hybrid algorithm (grey wolf optimization - intelligent bug algorithm GWO-IBA) to determine the best, shortest and without obstacles path. The hybrid algorithm was implemented and tested in the MATLAB program on the Tri-copter model, and it gave different paths in different environments. The paths obtained were characterized by being free of obstacles and the shortest paths available to reach the target
Multi-Objective Multi-Agent Planning for Jointly Discovering and Tracking Mobile Object
We consider the challenging problem of online planning for a team of agents
to autonomously search and track a time-varying number of mobile objects under
the practical constraint of detection range limited onboard sensors. A standard
POMDP with a value function that either encourages discovery or accurate
tracking of mobile objects is inadequate to simultaneously meet the conflicting
goals of searching for undiscovered mobile objects whilst keeping track of
discovered objects. The planning problem is further complicated by
misdetections or false detections of objects caused by range limited sensors
and noise inherent to sensor measurements. We formulate a novel multi-objective
POMDP based on information theoretic criteria, and an online multi-object
tracking filter for the problem. Since controlling multi-agent is a well known
combinatorial optimization problem, assigning control actions to agents
necessitates a greedy algorithm. We prove that our proposed multi-objective
value function is a monotone submodular set function; consequently, the greedy
algorithm can achieve a (1-1/e) approximation for maximizing the submodular
multi-objective function.Comment: Accepted for publication to the Thirty-Fourth AAAI Conference on
Artificial Intelligence (AAAI-20). Added algorithm 1, background on MPOMDP
and OSP
Detecting animals in African Savanna with UAVs and the crowds
Unmanned aerial vehicles (UAVs) offer new opportunities for wildlife
monitoring, with several advantages over traditional field-based methods. They
have readily been used to count birds, marine mammals and large herbivores in
different environments, tasks which are routinely performed through manual
counting in large collections of images. In this paper, we propose a
semi-automatic system able to detect large mammals in semi-arid Savanna. It
relies on an animal-detection system based on machine learning, trained with
crowd-sourced annotations provided by volunteers who manually interpreted
sub-decimeter resolution color images. The system achieves a high recall rate
and a human operator can then eliminate false detections with limited effort.
Our system provides good perspectives for the development of data-driven
management practices in wildlife conservation. It shows that the detection of
large mammals in semi-arid Savanna can be approached by processing data
provided by standard RGB cameras mounted on affordable fixed wings UAVs
Joint Route Optimization and Multidimensional Resource Management Scheme for Airborne Radar Network in Target Tracking Application
In this article, we investigate the problem of joint route optimization and multidimensional resource management (JRO-MDRM) for an airborne radar network in target tracking application. The mechanism of the proposed JRO-MDRM scheme is to adopt the optimization technique to collaboratively design the flight route, transmit power, dwell time, waveform bandwidth, and pulselength of each airborne radar node subject to the system kinematic limitations and several resource budgets, with the aim of simultaneously enhancing the target tracking accuracy and low probability of intercept (LPI) performance of the overall system. The predicted Bayesian Cramér–Rao lower bound and the probability of intercept are calculated and employed as the metrics to gauge the target tracking performance and LPI performance, respectively. It is shown that the resulting optimization problem is nonlinear and nonconvex, and the corresponding working parameters are coupled in both objective functions, which is generally intractable. By incorporating the particle swarm optimization and cyclic minimization approaches, an efficient four-step solution algorithm is proposed to deal with the above problem. Extensive numerical results are provided to demonstrate the correctness and advantages of our developed scheme compared with other existing benchmarks
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