Automatic collision avoidance using model-predictive online optimization

Werling, Moritz; Liccardo, Darren

doi:10.1109/cdc.2012.6426612

Cited by 64 publications

(33 citation statements)

References 16 publications

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“…An MPC-based obstacle avoidance algorithm for unmanned aerial vehicles was first reported in [13]. Other examples of MPC-based obstacle avoidance for autonomous vehicle systems can be found in [14], [15] and [16]. MPC-based strategies find optimal actions by formulating and solving an optimization problem over a rolling horizon window into the future.…”

Section: Introductionmentioning

confidence: 98%

Teleoperation of a mobile robot with model-predictive obstacle avoidance control

Salmanipour

Sirouspour

2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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This paper presents a mixed teleoperation/autonomous control approach for navigation and obstacle avoidance in mobile robots. The proposed method builds on an earlier general control framework that systematically combines teleoperation and autonomous control subtasks. This paper considers a scenario in which the user teleoperates a mobile robot while being assisted by an autonomous subtask designed to help avoid collisions with obstacles in the robot task environment. The autonomous subtask control commands are generated by formulating and solving a constrained optimization problem over a rolling horizon window of time into the future. The effectiveness of the proposed model-predictive control obstacle avoidance scheme is demonstrated in teleoperation experiments with a mobile robot using sonar measurements for obstacle localization.

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Section: Introductionmentioning

confidence: 98%

Teleoperation of a mobile robot with model-predictive obstacle avoidance control

Salmanipour

Sirouspour

2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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“…Moreover, we assume that there is a measurement uncertainty for each state, see (2). The characteristic velocity v ch is a parameter which computes as v ch = l 2 cfcr m(crlr−cflf) , with c f , c r denoting the cornering stiffness of the front and rear wheels; l f , l r the distances between the front and rear axis to the center of gravity; l = l f +l r their sum; and m the vehicle mass [37].…”

Section: Controller Designmentioning

confidence: 99%

“…For computing the motion primitives, we consider the steady state vehicle model (SSM) proposed in [37] together with disturbances and input noise, which accounts for any uncertainties and model mismatch to the real vehicle:…”

Section: Controller Designmentioning

confidence: 99%

Ensuring drivability of planned motions using formal methods

Schürmann

Hes

Eilbrecht

et al. 2017

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

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Abstract-Motion planning of automated vehicles requires dynamical models to ensure that obtained trajectories are drivable. An often overlooked aspect is that motion planning is usually done using simplified models, which do not always sufficiently conform to the real behavior of vehicles. Thus, collision avoidance and drivability is not necessarily ensured. We address this problem by modeling vehicles as differential inclusions composed of simple dynamics and set-based uncertainty; conformance testing is used to determine the required uncertainty. To quickly provide the set of solutions for these uncertain models, we use precomputed reachable sets (i.e., the union of all possible solutions) for pre-selected motion primitives. The reachable sets of vehicles are obtained through the novel combination of optimization techniques and reachability analysis in the controller synthesis -they enable us to guarantee safety by checking their mutual non-intersection for consecutive time intervals. The benefits of our approach are demonstrated by numerical experiments.

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“…An algorithm which generates collisionfree trajectories in a static environment is proposed in [20]. In [21], collision avoidance is achieved through steering and braking, under the assumption that the obstacles move with constant velocity.…”

Section: Introductionmentioning

confidence: 99%

“…For computing optimal trajectories which consider constraints, different approaches such as optimal control or MPC can be used [19]- [21]. In [19], MPC is utilized for lane departure prevention.…”

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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Automatic collision avoidance using model-predictive online optimization

Cited by 64 publications

References 16 publications

Teleoperation of a mobile robot with model-predictive obstacle avoidance control

Teleoperation of a mobile robot with model-predictive obstacle avoidance control

Ensuring drivability of planned motions using formal methods

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

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