Path Planning Using Probability Tensor Flows

Palmieri, F.; Pattipati, Krishna R.; Fioretti, Giovanni; Gennaro, Giovanni Di; Buonanno, Amedeo

doi:10.1109/maes.2020.3032069

Cited by 2 publications

(4 citation statements)

References 25 publications

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“…We have successfully demonstrated this approach for path planning in our previous work [30]. In fact, in the progressive max posterior algorithm, the forward flow is not necessary.…”

Section: B Progressive Max Posterior Sequencesmentioning

confidence: 94%

“…We have proposed in some of our previous works various techniques for modeling the motion behaviors of pedestrians and ships [25]- [29]. More recently, while experimenting with probability propagation in path planning problms [30], we came to realize that the probabilistic algorithms may be the most promising approaches for agile modeling of intelligent agent motion in complex scenes. This led to the development of the unified belief propagation framework for estimation and control discussed in this paper.…”

Section: B the Path Modeling Problemmentioning

confidence: 99%

“…Even if they are often used in the applications (for example in decoding convolutional codesthe algorithm, is named BCJR after its authors [37]), they may provide unsatisfactory sequences for path planning. In fact, the sequences that result from the individual (local) maximizations are unconstrained in time and may correspond to disconnected paths [30].…”

Section: A Max Posterior Sequencesmentioning

confidence: 99%

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A Unifying View of Estimation and Control Using Belief Propagation With Application to Path Planning

et al. 2022

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The use of estimation techniques on stochastic models to solve control problems is an emerging paradigm that falls under the rubric of Active Inference (AI) and Control as Inference (CAI). In this work, we use probability propagation on factor graphs to show that various algorithms proposed in the literature can be seen as specific composition rules in a factor graph. We show how this unified approach, presented both in probability space and in log of the probability space, provides a very general framework that includes the Sum-product, the Max-product, Dynamic programming and mixed Reward/Entropy criteria-based algorithms. The framework also expands algorithmic design options that lead to new smoother or sharper policy distributions. We propose original recursions such as: a generalized Sum/Max-product algorithm, a Smooth Dynamic programming algorithm and a modified versions of the Reward/Entropy algorithm. The discussion is carried over with reference to a path planning problem where the recursions that arise from various cost functions, although they may appear similar in scope, bear noticeable differences. We provide a comprehensive table of composition rules and a comparison through simulations, first on a synthetic small grid with a single goal with obstacles, and then on a grid extrapolated from a real-world scene with multiple goals and a semantic map.

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“…We have successfully demonstrated this approach for path planning in our previous work [30]. In fact, in the progressive max posterior algorithm, the forward flow is not necessary.…”

Section: B Progressive Max Posterior Sequencesmentioning

confidence: 94%

Section: B the Path Modeling Problemmentioning

confidence: 99%

Section: A Max Posterior Sequencesmentioning

confidence: 99%

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A Unifying View of Estimation and Control Using Belief Propagation With Application to Path Planning

et al. 2022

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“…the FGrn autonomously modifies its behavior by carrying out a process of pure diffusion which determines the best possible trajectory to reach a given state in a finite number of steps. Note that this propagation process leads to optimality only if we are interested in evaluating the minimum-time path [28]. 8 Although the reward is accumulated via c (s t , a t ), the forward process totally ignores it, not being able to consider other nonminimal paths that could accumulate larger rewards.…”

Section: The Forward Propagationmentioning

confidence: 99%

Probabilistic Inference and Dynamic Programming: A Unified Approach to Multi-Agent Autonomous Coordination in Complex and Uncertain Environments

et al. 2022

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We present a unified approach to multi-agent autonomous coordination in complex and uncertain environments, using path planning as a problem context. We start by posing the problem on a probabilistic factor graph, showing how various path planning algorithms can be translated into specific message composition rules. This unified approach provides a very general framework that, in addition to including standard algorithms (such as sum-product, max-product, dynamic programming and mixed Reward/Entropy criteria-based algorithms), expands the design options for smoother or sharper distributions (resulting in a generalized sum/max-product algorithm, a smooth dynamic programming algorithm and a modified versions of the reward/entropy recursions). The main purpose of this contribution is to extend this framework to a multi-agent system, which by its nature defines a totally different context. Indeed, when there are interdependencies among the key elements of a hybrid team (such as goals, changing mission environment, assets and threats/obstacles/constraints), interactive optimization algorithms should provide the tools for producing intelligent courses of action that are congruent with and overcome bounded rationality and cognitive biases inherent in human decision-making. Our work, using path planning as a domain of application, seeks to make progress towards this aim by providing a scientifically rigorous algorithmic framework for proactive agent autonomy.

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Path Planning Using Probability Tensor Flows

Cited by 2 publications

References 25 publications

A Unifying View of Estimation and Control Using Belief Propagation With Application to Path Planning

A Unifying View of Estimation and Control Using Belief Propagation With Application to Path Planning

Probabilistic Inference and Dynamic Programming: A Unified Approach to Multi-Agent Autonomous Coordination in Complex and Uncertain Environments

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