Risk aversion in belief-space planning under measurement acquisition uncertainty

Chaves, Stephen; Walls, Jeffrey M.; Galceran, Enric; Eustice, Ryan M.

doi:10.1109/iros.2015.7353653

Cited by 18 publications

(14 citation statements)

References 27 publications

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“…A few works [16]- [18] consider randomness in measurement acquisition, which is similar to the sparse informative measurements problem. Patil et al [16] introduces signed distance field to model sensing regions.…”

Section: Related Workmentioning

confidence: 99%

Belief Space Planning for Mobile Robots with Range Sensors using iLQG

Sun

Kumar

2021

Preprint

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In this work, we use iterative Linear Quadratic Gaussian (iLQG) to plan motions for a mobile robot with range sensors in belief space. We address two limitations that prevent applications of iLQG to the considered robotic system. First, iLQG assumes a differentiable measurement model, which is not true for range sensors. We show that iLQG only requires the differentiability of the belief dynamics. We propose to use a derivative-free filter to approximate the belief dynamics, which does not require explicit differentiability of the measurement model. Second, informative measurements from a range sensor are sparse. Uninformative measurements produce trivial gradient information, which prevent iLQG optimization from converging to a local minimum. We densify the informative measurements by introducing additional parameters in the measurement model. The parameters are iteratively updated in the optimization to ensure convergence to the true measurement model of a range sensor. We show the effectiveness of the proposed modifications through an ablation study. We also apply the proposed method in simulations of large scale real world environments, which show superior performance comparing to the state-of-the-art methods that either assume the separation principle or maximum likelihood measurements.

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Section: Related Workmentioning

confidence: 99%

Belief Space Planning for Mobile Robots with Range Sensors using iLQG

Sun

Kumar

2021

Preprint

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“…Reasoning probabilistically in robotic planning allows performance to degrade gracefully when encountering the unexpected. Notable work has been done on incorporating uncertainty from sensors into the state estimation of the system (Kalman, 1960; Kurniawati et al, 2008), or in the path planning itself (Bry and Roy, 2011; Chaves et al, 2015). However, uncertainty can lie in both the state and the world model, so we must address both sources of uncertainty to plan effectively.…”

Section: Related Workmentioning

confidence: 99%

Risk-aware graph search with dynamic edge cost discovery

Chung

Smith

Skeele

et al. 2018

The International Journal of Robotics Research

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In this paper, we introduce a novel algorithm for incorporating uncertainty into lookahead planning. Our algorithm searches through connected graphs with uncertain edge costs represented by known probability distributions. As a robot moves through the graph, the true edge costs of adjacent edges are revealed to the planner prior to traversal. This locally revealed information allows the planner to improve performance by predicting the benefit of edge costs revealed in the future and updating the plan accordingly in an online manner. Our proposed algorithm, risk-aware graph search (RAGS), selects paths with high probability of yielding low costs based on the probability distributions of individual edge traversal costs. We analyze RAGS for its correctness and computational complexity and provide a bounding strategy to reduce its complexity. We then present results in an example search domain and report improved performance compared with traditional heuristic search techniques. Lastly, we implement the algorithm in both simulated missions and field trials using satellite imagery to demonstrate the benefits of risk-aware planning through uncertain terrain for low-flying unmanned aerial vehicles.

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“…Under this assumption, the posterior information matrix normalΛ k + L is independent of (unknown) future observations (Indelman et al, 2015). One can further incorporate reasoning if a future measurement will indeed be acquired (Chaves et al, 2015; Indelman et al, 2015; Walls et al, 2015); however, this is outside the scope of this paper.…”

Section: Notation and Problem Definitionmentioning

confidence: 99%

No belief propagation required: Belief space planning in high-dimensional state spaces via factor graphs, the matrix determinant lemma, and re-use of calculation

Kopitkov

Indelman

2017

The International Journal of Robotics Research

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We develop a computationally efficient approach for evaluating the information-theoretic term within belief space planning (BSP), where during belief propagation the state vector can be constant or augmented. We consider both unfocused and focused problem settings, whereas uncertainty reduction of the entire system or only of chosen variables is of interest, respectively. State-of-the-art approaches typically propagate the belief state, for each candidate action, through calculation of the posterior information (or covariance) matrix and subsequently compute its determinant (required for entropy). In contrast, our approach reduces runtime complexity by avoiding these calculations. We formulate the problem in terms of factor graphs and show that belief propagation is not needed, requiring instead a one-time calculation that depends on (the increasing with time) state dimensionality, and per-candidate calculations that are independent of the latter. To that end, we develop an augmented version of the matrix determinant lemma, and show that computations can be re-used when evaluating impact of different candidate actions. These two key ingredients and the factor graph representation of the problem result in a computationally efficient (augmented) BSP approach that accounts for different sources of uncertainty and can be used with various sensing modalities. We examine the unfocused and focused instances of our approach, and compare it with the state of the art, in simulation and using real-world data, considering problems such as autonomous navigation in unknown environments, measurement selection and sensor deployment. We show that our approach significantly reduces running time without any compromise in performance.

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Risk aversion in belief-space planning under measurement acquisition uncertainty

Cited by 18 publications

References 27 publications

Belief Space Planning for Mobile Robots with Range Sensors using iLQG

Belief Space Planning for Mobile Robots with Range Sensors using iLQG

Risk-aware graph search with dynamic edge cost discovery

No belief propagation required: Belief space planning in high-dimensional state spaces via factor graphs, the matrix determinant lemma, and re-use of calculation

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