Backtracking Hybrid Α* for Resource Constrained Path Planning

Ford, Bryce T.; Aggarwal, R. K.; Kumar, Mrinal; Manyam, Satyanarayana G.; Casbeer, David W.; Grymin, David J.

doi:10.2514/6.2022-1592

Cited by 7 publications

(9 citation statements)

References 11 publications

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“…Here, the graph carries arc (or vertex) weights and the desired s-t path must not accumulate more than a given threshold of that weight. The problem is known to be NP-hard (Handler and Zang 1980) and studied in many variations (Ford et al 2022;Irnich and Desaulniers 2005;Pugliese and Guerriero 2013). A variation close to our setting introduces so-called replenishment arcs, at which one may "drop off" the weight accumulated so far (Smith, Boland, and Waterer 2012).…”

Section: S Tmentioning

confidence: 99%

Locally Rainbow Paths

Fluschnik,

Kellerhals,

Renken

2024

AAAI

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We introduce the algorithmic problem of finding a locally rainbow path of length l connecting two distinguished vertices s and t in a vertex-colored directed graph. Herein, a path is locally rainbow if between any two visits of equally colored vertices, the path traverses consecutively at leaset r differently colored vertices. This problem generalizes the well-known problem of finding a rainbow path. It finds natural applications whenever there are different types of resources that must be protected from overuse, such as crop sequence optimization or production process scheduling. We show that the problem is computationally intractable even if r=2 or if one looks for a locally rainbow among the shortest paths. On the positive side, if one looks for a path that takes only a short detour (i.e., it is slightly longer than the shortest path) and if r is small, the problem can be solved efficiently. Indeed, the running time of the respective algorithm is near-optimal unless the ETH fails.

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Section: S Tmentioning

confidence: 99%

Locally Rainbow Paths

Fluschnik,

Kellerhals,

Renken

2024

AAAI

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“…This algorithm only considers all events until the path is found. Backtracking Hybrid A + is a recently developed improvement on Hybrid A + (Ford et al, 2022) to allow for the efficient solution of resource-constrained path planning problems. Backtracking Hybrid A + operates on the principle that the violation of the loading constraint (Eq.…”

Section: Figurementioning

confidence: 99%

“…In the resource-constrained shortest path problem, the agent is looking for the path of minimum length while being constrained by either internal factors such as fuel consumption or external factors such as vehicular damage or noise constraints (Thomas et al, 2019). Resource constraints, also known as "path loading constraints", are integral constraints that are path dependent and accrue as the agent moves towards the goal pose (Ford et al, 2022). For instance, resource constraints can be used to place a limit on duration of exposure of a UAV leading to detection by radars while flying through adversarial territory (Kabamba et al, 2006;Zabarankin et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

“…The alternative is to allow the UAV to fly through high heat flux contours, while ensuring that the damage incurred during the flight is acceptable. Here, the path integral of heatflux contours at flight level equates to the rise in UAV temperature, which must be held under a prescribed limit (Ford et al, 2022) for safe operation. A good analogy is moving one's finger around a candle flame versus moving it through the flame following a trajectory that ensures no damage is done.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, instead of classifying the candidate node as "inadmissible", it reverses the search, receding away from the candidate node, thereby allowing the agent to shed the integral load along the path leading up to the constraint violation. Upon meeting an appropriately chosen stopping criterion for backtracking, the hybrid-Ap search resumes until another load violation is encountered (or the goal is reached) Ford et al (2022). This work ensures that during the graph search and its backtracking process, the dynamic obstacles' information corresponding to the current time-stamp is employed.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid A* path search with resource constraints and dynamic obstacles

Cortez

Ford

Nayak

et al. 2023

Front. Aerosp. Eng.

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This paper considers path planning with resource constraints and dynamic obstacles for an unmanned aerial vehicle (UAV), modeled as a Dubins agent. Incorporating these complex constraints at the guidance stage expands the scope of operations of UAVs in challenging environments containing path-dependent integral constraints and time-varying obstacles. Path-dependent integral constraints, also known as resource constraints, can occur when the UAV is subject to a hazardous environment that exposes it to cumulative damage over its traversed path. The noise penalty function was selected as the resource constraint for this study, which was modeled as a path integral that exerts a path-dependent load on the UAV, stipulated to not exceed an upper bound. Weather phenomena such as storms, turbulence and ice are modeled as dynamic obstacles. In this paper, ice data from the Aviation Weather Service is employed to create training data sets for learning the dynamics of ice phenomena. Dynamic mode decomposition (DMD) is used to learn and forecast the evolution of ice conditions at flight level. This approach is presented as a computationally scalable means of propagating obstacle dynamics. The reduced order DMD representation of time-varying ice obstacles is integrated with a recently developed backtracking hybrid A∗ graph search algorithm. The backtracking mechanism allows us to determine a feasible path in a computationally scalable manner in the presence of resource constraints. Illustrative numerical results are presented to demonstrate the effectiveness of the proposed path-planning method.

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