Adaptive Time Stepping in Real-Time Motion Planning

Hauser, Kristoffer Karl

doi:10.1007/978-3-642-17452-0_9

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…First, given that a replanning framework is used, a partial trajectory may not be sufficient to provide a solution. Recent work, however, has shown that for a single robot it is possible to provide probabilistic completeness guarantees within a replanning framework as long as the duration of a planning cycle can be adapted (Hauser, 2010). This was the motivating point for this work to show that safety can be guaranteed when robots adapt their planning cycles on the fly.…”

Section: Discussionmentioning

confidence: 99%

“…While highly desirable in real-world applications and necessary for scalability, the current decentralized solution does not reason in the composite state space of all the robots, which is necessary for completeness. One direction for resolving this issue is to consider a hybrid solution that integrates both decentralized and centralized planning, as in the work on dynamic robot networks (Clark et al, 2003), while also allowing for adaptive cycles (Hauser, 2010). Such a scheme would allow robots to operate in a decentralized manner when they can make progress.…”

Section: Discussionmentioning

confidence: 99%

“…A new proof of safety is provided under these conditions. The motivation for allowing the planning cycles to change comes from work on replanning for individual robots that has shown that adapting the duration of cycles can provide completeness guarantees (Hauser, 2010). Secondly, this paper also improves the upper bound on the robots' velocity required for safety.…”

Section: Contributionsmentioning

confidence: 99%

See 2 more Smart Citations

Safe distributed motion coordination for second-order systems with different planning cycles

Bekris

Grady

Moll

et al. 2012

The International Journal of Robotics Research

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When multiple robots operate in the same environment, it is desirable for scalability purposes to coordinate their motion in a distributed fashion while providing guarantees about their safety. If the robots have to respect second-order dynamics, this becomes a challenging problem, even for static environments. This work presents a replanning framework where each robot computes partial plans during each cycle, while executing a previously computed trajectory. Each robot communicates with its neighbors to select a trajectory that is safe over an infinite time horizon. The proposed approach does not require synchronization and allows the robots to dynamically change their cycles over time. This paper proves that the proposed motion coordination algorithm guarantees safety under this general setting. This framework is not specific to the underlying robot dynamics, as it can be used with a variety of dynamical systems, nor to the planning or control algorithm used to generate the robot trajectories. The performance of the approach is evaluated using a distributed multi-robot simulator on a computing cluster, where the simulated robots are forced to communicate by exchanging messages. The simulation results confirm the safety of the algorithm in various environments with up to 32 robots governed by second-order dynamics.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

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Safe distributed motion coordination for second-order systems with different planning cycles

Bekris

Grady

Moll

et al. 2012

The International Journal of Robotics Research

View full text Add to dashboard Cite

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“…Although the MPC method provides a good control effectiveness in their relative applications, it usually assumes a simple road topography, which means that it ignores the road curvature and tilt angle. Zhang et al [15] and Hauser [16] proposed an adaptive MPC method. The control time domain of the algorithm is automatically revised.…”

Section: Introductionmentioning

confidence: 99%

A Vehicle Trajectory Tracking Method With a Time-Varying Model Based on the Model Predictive Control

Shao

2020

IEEE Access

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The vehicle trajectory tracking algorithm is one of the key and difficult issues of intelligent driving technologies. In current control algorithms for the vehicle trajectory tracking, there are three main assumptions: the standard working condition for the driving path, the same control model used for the entire control process, and a fixed value for the longitudinal vehicle speed. However, the above determinations in current control algorithms are inconsistent with the actual vehicle driving conditions. To overcome those problems, a vehicle trajectory tracking method with a time-varying model is proposed. The time-varying model is developed by using a two-dimensional vehicle kinematics model. This method considers the influences of the longitudinal speed and road curvature on the vehicle trajectory tracking stability under the low-speed complex driving condition. Thus, the proposed method can improve the trajectory tracking accuracy when the unmanned vehicle is located at the road with a sharp curvature under the low-speed complex driving condition. Moreover, the proposed model can achieve the real time calculation. Meanwhile, the prediction accuracy of the vehicle kinematics model is ensured. The proposed approach with the above characteristics can complete the trajectory tracking for the route composed of arbitrary curves. The results show that the proposed method can effectively improve the trajectory tracking stability of the unmanned vehicle on the roads with different curvatures under complex driving conditions. INDEX TERMS Model predictive control, stability, time-varying model control, vehicle trajectory tracking method.

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“…Instead, replanning is done on the fly when a dangerous situation might already be inevitable. The authors of [22] propose an algorithm that computes an adaptive time horizon which dictates when replanning should be performed. The so-called "time to potential failure" determines how long the current trajectory is safe under some given assumptions.…”

Section: Introductionmentioning

confidence: 99%

Determining the maximum time horizon for vehicles to safely follow a trajectory

Magdici

Althoff

2017

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

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Abstract-Dealing with the unknown future behavior of other traffic participants is one of the main challenges when generating safe trajectories for autonomous vehicles. When the ego vehicle (i.e., the vehicle to be controlled) follows a given trajectory, an emergency maneuver should be kept available for all times in order to avoid collisions. However, generating an emergency maneuver for each time step is computationally expensive and often not required. In this paper, we propose an algorithm for determining the maximum time horizon under which the ego vehicle can safely follow a given trajectory. First, an upper and a lower bound of this time horizon are computed. Then, binary search is used to find the maximum time horizon for which safety is still guaranteed. Our algorithm reduces the frequency of generating emergency maneuvers while still guaranteeing collision-free trajectories. The approach is tested on real traffic data, and it is shown that our algorithm indeed reduces the frequency of generating emergency maneuvers compared to previous work.

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Adaptive Time Stepping in Real-Time Motion Planning

Cited by 5 publications

References 17 publications

Safe distributed motion coordination for second-order systems with different planning cycles

Safe distributed motion coordination for second-order systems with different planning cycles

A Vehicle Trajectory Tracking Method With a Time-Varying Model Based on the Model Predictive Control

Determining the maximum time horizon for vehicles to safely follow a trajectory

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