Simultaneous Non-Myopic Optimization of UAV Guidance and Camera Gimbal Control for Target Tracking

Krakow, Lucas W.; Eaton, Christopher M.; Chong, Edwin K. P.

doi:10.1109/ccta.2018.8511346

Cited by 7 publications

(8 citation statements)

References 7 publications

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“…A Partial Observable Markov Decision Process (POMDP) model is formulated in [15], which describes a non-myopic sensory centralized multi agent motion planning with the task of target tracking. This POMDP model is extended to the multi agent scenario with distributed planning in this paper.…”

Section: Methods a A Pomdp Modelmentioning

confidence: 99%

“…where G(ρ) is the rotation matrix of angle ρ, and sensing quality factor α i is a scalar in this uncertainty matrix. This imposes the spatially varying measurement error [15]. To avert computational issues inside the Kalman filter updates, we apply r 0 as the minimal effective range threshold.…”

Section: A Distributed Target Trackingmentioning

confidence: 99%

“…Firstly a good choice of information utility is essential to picture the objective of the system in a mathematical formation. The existing choices includes entropy [1] , Fisher Information matrix [8], the covariance matrix in Gaussian [10], [11], [15] and other sensor model-based heuristics [20]. In our problem, the trace of covariance matrix of targets is chosen for its perceptual intuition and ease of computation in the Gaussian based data association.…”

Section: B Information-driven Multiagent Coordination Controlmentioning

confidence: 99%

See 2 more Smart Citations

Optimizing Consensus-based Multi-target Tracking with Multiagent Rollout Control Policies

Li,

Krakow,

Gopalswamy

2021

Preprint

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This paper considers a multiagent, connected, robotic fleet where the primary functionality of the agents is sensing. A distributed multi-sensor control strategy maximizes the value of the collective sensing capability of the fleet, using an information-driven approach. Each agent individually performs sensor processing (Kalman Filtering and Joint Probabilistic Data Association) to identify trajectories (and associated distributions). Using communications with its neighbors the agents enhance the prediction of the trajectories using a Consensus of Information approach that iteratively calculates the Kullback-Leibler average of trajectory distributions, enabling the calculation of the value of the collective information for the fleet. The dynamics of the agents, the evolution of the identified trajectories for each agent, and the dynamics of individual observed objects are captured as a Partially Observable Markov Decision Process (POMDP). Using this POMDP and applying rollout with receding horizon control, an optimized non-myopic control policy that maximizes the collective fleet information value is synthesized. Simulations are performed for a scenario with three heterogeneous UAVs performing coordinated target tracking that illustrate the proposed methodology and compare the centralized approach with a contemporary sequential multiagent distributed decision technique.

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Section: Methods a A Pomdp Modelmentioning

confidence: 99%

Section: A Distributed Target Trackingmentioning

confidence: 99%

Section: B Information-driven Multiagent Coordination Controlmentioning

confidence: 99%

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Optimizing Consensus-based Multi-target Tracking with Multiagent Rollout Control Policies

Li,

Krakow,

Gopalswamy

2021

Preprint

Self Cite

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“…The observation model ( 3) for a given sensor can be extended for all the targets it observes to obtain the block diagonal H and R matrices for each agent. This observation setup imposes the spatially varying measurement error [7]. With observation law defined, the observation of n agents at each time step is…”

Section: Problem Definitionmentioning

confidence: 99%

“…In information theory, entropy is one solid mathematical quantification of information gathering and is treated as the objective to minimize via observation selection and action optimization [2], [5]. Another approach to target tracking seeks to plan the robot motion towards reduction of the mean squared error (MSE) of associated targets, which leads to the trace of covariance matrix tr [P] as the indicator of planning [6], [7], [8], [9].…”

Section: Introductionmentioning

confidence: 99%

SMA-NBO: A Sequential Multi-Agent Planning with Nominal Belief-State Optimization in Target Tracking

Li¹,

Krakow²,

Gopalswamy³

2022

Preprint

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In target tracking with mobile multi-sensor systems, sensor deployment impacts the observation capabilities and the resulting state estimation quality. Based on a partially observable Markov decision process (POMDP) formulation comprised of the observable sensor dynamics, unobservable target states, and accompanying observation laws, we present a distributed information-driven solution approach to the multi-agent target tracking problem, namely, sequential multi-agent nominal beliefstate optimization (SMA-NBO). SMA-NBO seeks to minimize the expected tracking error via receding horizon control including a heuristic expected cost-to-go (HECTG). SMA-NBO incorporates a computationally efficient approximation of the target beliefstate over the horizon. The agent-by-agent decision-making is capable of leveraging on-board (edge) compute for selecting (sub-optimal) target-tracking maneuvers exhibiting non-myopic cooperative fleet behavior. The optimization problem explicitly incorporates semantic information defining target occlusions from a world model. To illustrate the efficacy of our approach, a random occlusion forest environment is simulated. SMA-NBO is compared to other baseline approaches. The simulation results show SMA-NBO 1) maintains tracking performance and reduces the computational cost by replacing the calculation of the expected target trajectory with a single sample trajectory based on maximum a posteriori estimation; 2) generates cooperative fleet decision by sequentially optimizing single-agent policy with efficient usage of other agents' policy of intent; 3) aptly incorporates the multiple weighted trace penalty (MWTP) HECTG, which improves tracking performance with a computationally efficient heuristic.

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Optimizing Consensus-based Multi-target Tracking with Multiagent Rollout Control Policies

Krakow

Gopalswamy

2021

2021 IEEE Conference on Control Technology and Applications (CCTA)

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Tracking multiple moving objects of interest (OOI) with multiple robot systems (MRS) has been addressed by active sensing that maintains a shared belief of OOIs and plans the motion of robots to maximize the information quality. Mobility of robots enables the behavior of pursuing better visibility, which is constrained by sensor field of view (FoV) and occlusion objects. We first extend prior work to detect, maintain and share occlusion information explicitly, allowing us to generate occlusion-aware planning even if à priori semantic occlusion information is unavailable. The efficacy of active sensing approaches is often evaluated according to estimation error and information gain metrics. However, these metrics do not directly explain the level of cooperative behavior engendered by the active sensing algorithms. Next, we extract different emergent cooperative behaviors that stem from the same underlying algorithms but manifest differently under differing scenarios. In particular, we highlight and demonstrate three emergent behavior patterns in active sensing MRS: (i) Change of tracking responsibility between agents when tracking trajectories with divergent directions or due to a re-allocation of the resource among heterogeneous agents; (ii) Awareness of occlusions to a trajectory and temporal leave-and-return of the sensing agent; (iii) Sharing of local occlusion objects in MRS that subsequently improves the awareness of occlusion.

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Simultaneous Non-Myopic Optimization of UAV Guidance and Camera Gimbal Control for Target Tracking

Cited by 7 publications

References 7 publications

Optimizing Consensus-based Multi-target Tracking with Multiagent Rollout Control Policies

Optimizing Consensus-based Multi-target Tracking with Multiagent Rollout Control Policies

SMA-NBO: A Sequential Multi-Agent Planning with Nominal Belief-State Optimization in Target Tracking

Optimizing Consensus-based Multi-target Tracking with Multiagent Rollout Control Policies

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