2010
DOI: 10.1007/978-3-642-13408-1_7
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Receding Horizon Model-Predictive Control for Mobile Robot Navigation of Intricate Paths

Abstract: As mobile robots venture into more complex environments, more arbitrary feasible state-space trajectories and paths are required to move safely and efficiently. The capacity to effectively navigate such paths in the face of disturbances and changes in mobility can mean the difference between mission failure and success. This paper describes a technique for model predictive control of a mobile robot that utilizes the structure of a regional motion plan to effectively search the local continuum for an improved s… Show more

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Cited by 63 publications
(25 citation statements)
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“…In [21], the learning-based MPC uses a linear model with bounds on its uncertainty to construct invariant sets that provide deterministic guarantees on robustness and safety. In [22], an RH MPC technique was proposed to address path following and obstacle avoidance through geometric singularities and discontinuities. Research in [16] uses a linearized tracking-error dynamics to predict future system behavior, and uses a quadratic cost function of tracking error and the control effort to derive a control law.…”
Section: Introductionmentioning
confidence: 99%
“…In [21], the learning-based MPC uses a linear model with bounds on its uncertainty to construct invariant sets that provide deterministic guarantees on robustness and safety. In [22], an RH MPC technique was proposed to address path following and obstacle avoidance through geometric singularities and discontinuities. Research in [16] uses a linearized tracking-error dynamics to predict future system behavior, and uses a quadratic cost function of tracking error and the control effort to derive a control law.…”
Section: Introductionmentioning
confidence: 99%
“…Change-reactive planning techniques are also needed. Approaches have been created based on combining long-term and reactive approaches [23] and a receding horizon model [24]. For scenarios that fluctuate, inference [25] and constraint satisfaction [26] approaches have been proposed.…”
Section: Autonomous Command and Supporting Technologiesmentioning
confidence: 99%
“…Kelly and Nagy described a model predictive trajectory generation algorithm that generated a set of parameterized controls that could be directly executed by a vehicle controller [2]. Howard and Kelly described a Receding Horizon Model Predictive Control (RHMPC) that followed paths and avoided obstacles through geometric singularities and discontinuities [3]. Lacaze et al used Ego Graphs to generate a set of layered trajectories that could be directly executed by the vehicle controller [4].…”
Section: Related Workmentioning
confidence: 99%