7,138 research outputs found
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
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Anytime Planning for Decentralized Multirobot Active Information Gathering
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
The Smooth Trajectory Estimator for LMB Filters
This paper proposes a smooth-trajectory estimator for the labelled
multi-Bernoulli (LMB) filter by exploiting the special structure of the
generalised labelled multi-Bernoulli (GLMB) filter. We devise a simple and
intuitive approach to store the best association map when approximating the
GLMB random finite set (RFS) to the LMB RFS. In particular, we construct a
smooth-trajectory estimator (i.e., an estimator over the entire trajectories of
labelled estimates) for the LMB filter based on the history of the best
association map and all of the measurements up to the current time.
Experimental results under two challenging scenarios demonstrate significant
tracking accuracy improvements with negligible additional computational time
compared to the conventional LMB filter. The source code is publicly available
at https://tinyurl.com/ste-lmb, aimed at promoting advancements in MOT
algorithms.Comment: 6 pages, 5 figures. Presented at The 12th IEEE International
Conference on Control, Automation and Information Sciences (ICCAIS 2023), Nov
2023, Hanoi, Vietna
Versatile Multi-Contact Planning and Control for Legged Loco-Manipulation
Loco-manipulation planning skills are pivotal for expanding the utility of
robots in everyday environments. These skills can be assessed based on a
system's ability to coordinate complex holistic movements and multiple contact
interactions when solving different tasks. However, existing approaches have
been merely able to shape such behaviors with hand-crafted state machines,
densely engineered rewards, or pre-recorded expert demonstrations. Here, we
propose a minimally-guided framework that automatically discovers whole-body
trajectories jointly with contact schedules for solving general
loco-manipulation tasks in pre-modeled environments. The key insight is that
multi-modal problems of this nature can be formulated and treated within the
context of integrated Task and Motion Planning (TAMP). An effective bilevel
search strategy is achieved by incorporating domain-specific rules and
adequately combining the strengths of different planning techniques: trajectory
optimization and informed graph search coupled with sampling-based planning. We
showcase emergent behaviors for a quadrupedal mobile manipulator exploiting
both prehensile and non-prehensile interactions to perform real-world tasks
such as opening/closing heavy dishwashers and traversing spring-loaded doors.
These behaviors are also deployed on the real system using a two-layer
whole-body tracking controller
Online planning for multi-robot active perception with self-organising maps
© 2017, Springer Science+Business Media, LLC, part of Springer Nature. We propose a self-organising map (SOM) algorithm as a solution to a new multi-goal path planning problem for active perception and data collection tasks. We optimise paths for a multi-robot team that aims to maximally observe a set of nodes in the environment. The selected nodes are observed by visiting associated viewpoint regions defined by a sensor model. The key problem characteristics are that the viewpoint regions are overlapping polygonal continuous regions, each node has an observation reward, and the robots are constrained by travel budgets. The SOM algorithm jointly selects and allocates nodes to the robots and finds favourable sequences of sensing locations. The algorithm has a runtime complexity that is polynomial in the number of nodes to be observed and the magnitude of the relative weighting of rewards. We show empirically the runtime is sublinear in the number of robots. We demonstrate feasibility for the active perception task of observing a set of 3D objects. The viewpoint regions consider sensing ranges and self-occlusions, and the rewards are measured as discriminability in the ensemble of shape functions feature space. Exploration objectives for online tasks where the environment is only partially known in advance are modelled by introducing goal regions in unexplored space. Online replanning is performed efficiently by adapting previous solutions as new information becomes available. Simulations were performed using a 3D point-cloud dataset from a real robot in a large outdoor environment. Our results show the proposed methods enable multi-robot planning for online active perception tasks with continuous sets of candidate viewpoints and long planning horizons
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