State space sampling of feasible motions for high‐performance mobile robot navigation in complex environments

Howard, Thomas M.; Green, Colin J.; Kelly, Alonzo; Ferguson, Dave

doi:10.1002/rob.20244

Cited by 185 publications

(94 citation statements)

References 22 publications

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“…Desired states represent all the feasible terminal states. As [3] says states arise from the environment, not from vehicle mobility. Thus desired states sampling should consider environmental constraints.…”

Section: Motion Planning Algorithmmentioning

confidence: 99%

“…State space sampling method [3,4] and Frenet Frame based semi-reactive technique [5] is used to generate feasible trajectories on-line. They are both based on predictive control approach.…”

Section: Introductionmentioning

confidence: 99%

“…In this approach, motion planning has two key procedures: 1) trajectory generation, and 2) trajectory selection. The former generates motion planning search space, while the latter determines or selects the optimal or preferred trajectory.State space sampling method [3,4] and Frenet Frame based semi-reactive technique [5] is used to generate feasible trajectories on-line. They are both based on predictive control approach.…”

mentioning

confidence: 99%

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A Motion Planning Method for Autonomous Vehicles

Zhao¹,

Liu²,

Zhu³

et al. 2017

dtetr

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Abstract.A model based predictive motion planning with combining Bayesian Methodology for autonomous driving is proposed. The planning algorithm firstly generates candidate obstacle free trajectories among the current state and sampled desired states using trajectory generation techniques. Then Bayesian Methodology is applied to select the optimal trajectory among candidate trajectories. The preferred trajectory selected in the previous planning cycle is regarded as the prior knowledge, while the trajectory cost in current cycle is transformed into likelihood function. Both the distribution follows the normal distribution and the Bayesian theorem is adopted to calculate the posterior knowledge to determine the preferred trajectory. Then the speed profile is calculated with the preferred trajectory to produce the real trajectory. The proposed motion planner is implemented and tested in simulations. Experiments results show that the planner has good performance in autonomous driving and especially reduces indecision behavior in uncertain environments, and improves stability of autonomous driving. IntroductionThe motion planning is a fundamental technology in autonomous driving. The high performance of motion planner will improve greatly the performance of autonomous driving, especially stability, safety, smoothness, and speed. Because of the inherent dynamic-mechanism delay and hysteresis in autonomous systems, predictive approach [1, 2] is commonly utilized in motion planning. In this approach, motion planning has two key procedures: 1) trajectory generation, and 2) trajectory selection. The former generates motion planning search space, while the latter determines or selects the optimal or preferred trajectory.State space sampling method [3,4] and Frenet Frame based semi-reactive technique [5] is used to generate feasible trajectories on-line. They are both based on predictive control approach. Commonly, the performance of motion planner mainly depends on trajectory selection; however how to select the preferred trajectory is not mentioned clearly in above researches. In [5] the optimal trajectory was determined by utilizing optimal control approach, but the cost function was different in different behaviors which might make parameters tuning to be complex and superfluous.In this paper, an efficient motion planner combining trajectory generation using quintic polynomials with trajectory selection adopting Bayesian method is proposed. The planner has the following features: generated trajectories are satisfying differential, dynamic and road constraints, therefore are kinematically feasible, and are smooth and continuous enough due to high order polynomials; Bayesian based trajectory evaluation and selection deals with the indecision and unstable problem in planning, and guarantees time consistency with sequence knowledge which is formulated as probabilistic principles.

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Section: Motion Planning Algorithmmentioning

confidence: 99%

“…State space sampling method [3,4] and Frenet Frame based semi-reactive technique [5] is used to generate feasible trajectories on-line. They are both based on predictive control approach.…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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A Motion Planning Method for Autonomous Vehicles

Zhao¹,

Liu²,

Zhu³

et al. 2017

dtetr

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show abstract

“…In Phase 1, we planned using searches in a parametric representation of the velocity space of both the arm and neck angles, at the cost of additional computational complexity Howard et al 2008). The new planner runs faster and more easily handles constrained motions by sampling in the joint space of the two arms, but post-process smoothing of the motion is required.…”

Section: Arm Plannermentioning

confidence: 99%

Model-based autonomous system for performing dexterous, human-level manipulation tasks

Hudson

Jain

et al. 2013

Auton Robot

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This article presents a model based approach to autonomous dexterous manipulation, developed as part of the DARPA Autonomous Robotic Manipulation Software (ARM-S) program. Performing human-level manipulation tasks is achieved through a novel combination of perception in uncertain environments, precise tool use, forceful dualarm planning and control, persistent environmental tracking, and task level verification. Deliberate interaction with the environment is incorporated into planning and control strategies, which, when coupled with world estimation, allows for refinement of models and precise manipulation. The system takes advantage of sensory feedback immediately with little open-loop execution, attempting true autonomous reasoning and multi-step sequencing that adapts in the face of changing and uncertain environments. A tire change scenario utilizing human tools, discussed throughout the article, is used to described the system approach. A second scenario of cutting a wire is also presented, and is used to illustrate system component reuse and generality.

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“…The motion planner developed to generate paths from the current state to the target vehicle state followed the methodology outlined in [5]. An expressive search space is generated by intelligently sampling the state space of terminal vehicle configurations based on feasible base placements for the mobile manipulator.…”

Section: Motion Planningmentioning

confidence: 99%