Receding horizon robot control in partially unknown environments with temporal logic constraints

Nenchev, Vladislav; Belta, Călin

doi:10.1109/ecc.2016.7810684

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…Leveraging ideas from [37], [30], let J 2 :x| [t,t f ] × R a → R b be a continuously differentiable version of the mapping h that captures the constraints of the high-level OCP, such that J 2 (x * , θ) = 0 b , if h(x * , θ) = 0 b , and J 2 (x * , θ) > 0 b , else. We will now introduce the individual constraints of the optimization problem.…”

Section: B Parametric Optimizationmentioning

confidence: 99%

“…In this context, employing a discrete abstraction of the underlying continuous dynamics is often only possible by introducing a hierarchical decomposition [26], or additional assumptions that simplify the implementation of automatically synthesized hybrid controllers [27]. Alternatively, one may resort to receding horizon approaches that have been shown to outperform other optimization methods under the presence of uncertainty, e.g., for the elevator dispatching problem [28], multi-agent reward collection problems [29] or planning with temporal logic constraints [30]. For a scenario where the number of objects is finite but unknown, a combined optimal exploration and control scheme for a robot that has to find, collect and move objects in a two-dimensional position space was proposed in [31].…”

Section: Introductionmentioning

confidence: 99%

“…Compute Ĵ and ∇ θ Ĵ| Yg with µ z , and update θ through (38) and λz through (30). Set µ z+1 = νµ z .…”

mentioning

confidence: 99%

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Optimal exploration and control for a robotic pick-up and delivery problem

Nenchev

Cassandras

2014

53rd IEEE Conference on Decision and Control

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This paper addresses an optimal control problem for a robot that has to find and collect a finite number of objects and move them to a depot in minimum time. The robot has fourthorder dynamics that change instantaneously at any pick-up or drop-off of an object. The objects are modeled by point masses with a-priori unknown locations in a bounded two-dimensional space that may contain unknown obstacles. For this hybrid system, an Optimal Control Problem (OCP) is approximately solved by a receding horizon scheme, where the derived lower bound for the cost-to-go is evaluated for the worst and for a probabilistic case, assuming a uniform distribution of the objects. First, a time-driven approximate solution based on time and position space discretization and mixed integer programming is presented. Due to the high computational cost of this solution, an alternative event-driven approximate approach based on a suitable motion parameterization and gradient-based optimization is proposed. The solutions are compared in a numerical example, suggesting that the latter approach offers a significant computational advantage while yielding similar qualitative results compared to the former. The methods are particularly relevant for various robotic applications like automated cleaning, search and rescue, harvesting or manufacturing.

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Section: B Parametric Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal exploration and control for a robotic pick-up and delivery problem

Nenchev

Cassandras

2014

53rd IEEE Conference on Decision and Control

Self Cite

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“…Motion planning in partially known environments [1] is a challenging problem in robotics and automation, where a robot needs to navigate through an environment with limited or uncertain information about its surroundings. To address this challenge, the majority of literature proposed several approaches under temporal logic specifications [2,3] by taking advantages that they are able to specify high-level goals that the robot must satisfy over time [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A leader‐follower communication protocol for motion planning in partially known environments under temporal logic specifications

Yan,

Liu,

Chen

et al. 2024

IET Control Theory & Appl

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This paper considers the problem of communication protocols between leaders and its followers for motion planning in an initially partially known environment. In this setting, the leader observes the environment information to satisfy its own local objective and and the follower completes its own local objective by estimating the states of the leader and communicating with the leader to update its knowledge about the environment when it is necessary, where the local objectives can be expressed in temporal logic. A verifier construction is built first to contain all possible communication protocols between the leaders and the followers. Then, a two‐step synthesis procedure is proposed to capture all feasible communication protocol that satisfy the local objectives for the leader and follower, respectively. In the first step, a sub‐verifier is synthesized to satisfy the objective of the follower. In the second step, based on the obtained sub‐verifier, an iterative algorithm is proposed to extract communication protocols such that the objectives of the leader and follower are satisfied, respectively. A running example is provided to illustrate the proposed procedures.

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“…This necessity has led to the increased use of formal methods like Linear Temporal Logic (LTL), Computation Tree Logic (CTL), and µ-calculus to precisely define robot mission requirements [15][16][17]. LTL, in particular, has become prominent for its ability to clearly articulate robotic objectives [18][19][20][21]. For instance, LTL formulas like ϕ = ♢(A) ∧ ♢(B) explicitly denote tasks such as delivering items to region (A) and collecting items from region (B).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Enhanced Sampling-Based Path Planning for LTL Mission Specifications

Baek,

Cho

2024

Sensors

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The presented paper introduces a novel path planning algorithm designed for generating low-cost trajectories that fulfill mission requirements expressed in Linear Temporal Logic (LTL). The proposed algorithm is particularly effective in environments where cost functions encompass the entire configuration space. A core contribution of this paper is the presentation of a refined approach to sampling-based path planning algorithms that aligns with the specified mission objectives. This enhancement is achieved through a multi-layered framework approach, enabling a simplified discrete abstraction without relying on mesh decomposition. This abstraction is especially beneficial in complex or high-dimensional environments where mesh decomposition is challenging. The discrete abstraction effectively guides the sampling process, influencing the selection of vertices for extension and target points for steering in each iteration. To further improve efficiency, the algorithm incorporates a deep learning-based extension, utilizing training data to accurately model the optimal trajectory distribution between two points. The effectiveness of the proposed method is demonstrated through simulated tests, which highlight its ability to identify low-cost trajectories that meet specific mission criteria. Comparative analyses also confirm the superiority of the proposed method compared to existing methods.

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Receding horizon robot control in partially unknown environments with temporal logic constraints

Cited by 7 publications

References 20 publications

Optimal exploration and control for a robotic pick-up and delivery problem

Optimal exploration and control for a robotic pick-up and delivery problem

A leader‐follower communication protocol for motion planning in partially known environments under temporal logic specifications

Deep Learning-Enhanced Sampling-Based Path Planning for LTL Mission Specifications

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