Dynamic risk tolerance: Motion planning by balancing short-term and long-term stochastic dynamic predictions

Chiang, Hao-Tien Lewis; HomChaudhuri, Baisravan; Vinod, Abraham P.; Oishi, Meeko; Tapia, Lydia

doi:10.1109/icra.2017.7989434

Cited by 16 publications

(12 citation statements)

References 24 publications

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“…Seder et.al [26] first adopt a focused D* to find a reference path without considering obstacle motions and then generates admissible local trajectories around the reference path using a dynamic window algorithm. In [27], the RRT and forward SR sets are utilized to find the reference path and avoid moving obstacles, respectively.…”

Section: B Dyanmic Planningmentioning

confidence: 99%

Autonomous Flights in Dynamic Environments with Onboard Vision

Wang

et al. 2021

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In this paper, we introduce a complete system for autonomous flight of quadrotors in dynamic environments with onboard sensing. Extended from existing work, we develop an occlusion-aware dynamic perception method based on depth images, which classifies obstacles as dynamic and static. For representing generic dynamic environment, we model dynamic objects with moving ellipsoids and fuse static ones into an occupancy grid map. To achieve dynamic avoidance, we design a planning method composed of modified kinodynamic path searching and gradient-based optimization. The method leverages manually constructed gradients without maintaining a signed distance field (SDF), making the planning procedure finished in milliseconds. We integrate the above methods into a customized quadrotor system and thoroughly test it in realworld experiments, verifying its effective collision avoidance in dynamic environments.

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Section: B Dyanmic Planningmentioning

confidence: 99%

Autonomous Flights in Dynamic Environments with Onboard Vision

Wang

et al. 2021

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…Therefore, setting the time window of the path planning method is not desirable. A framework for dynamic risk assessment is proposed in [37], in which the forward random reachable set is used to predict the distribution of obstacles in the time window and balance the risks caused by the near and far obstacles. In [38], a VO-based algorithm is presented to handle the high-speed obstacle in dynamic scenarios.…”

Section: A Related Workmentioning

confidence: 99%

Collision Avoidance of High-Speed Obstacles for Mobile Robots via Maximum-Speed Aware Velocity Obstacle Method

Xu¹,

Kordyuk²,

Jiang³

et al. 2020

IEEE Access

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It is challenging for a mobile robot to avoid moving obstacles in dynamic environments. Traditional velocity obstacle methods do not fully consider the obstacles moving with the speeds larger than the maximum speed of the robot. In this paper, a new obstacle avoidance method, named the maximum-speed aware velocity obstacle (MVO) algorithm, is proposed for a mobile robot to avoid one or multiple high-speed obstacles. The proposed algorithm expands the velocity obstacle region into two parts, where one of the parts foresees collisions beyond the time horizon to ensure the feasible solutions of the current and the next control step. In practical applications, the perception capability of the robot is generally limited, and a non-holonomic robot can't move into any direction due to its kinematic constraints. In this paper, the limited sensing field of view and non-holonomic kinematic constraints of the mobile robot are incorporated into the proposed MVO method. Moreover, continuity, safety, and computational complexity of the MVO approach are analyzed and presented. Extensive simulations and physical experiments are conducted to verify the efficacy of the MVO method, where a quadrotor and a differential-drive robot are used to perform dynamic obstacle avoidance.

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“…4a, we first transform the obstacle trajectory by subtracting ξ k , which can be easily implemented by using Eq. (8). Based on such a transformation, the problem is then converted to checking the minimum distance d between the current position of the robot and the transformed trajectory of the obstacle.…”

Section: Collision Checkingmentioning

confidence: 99%

“…In this way, the planning and execution procedures can be accurately scheduled to achieve fast re-planning. Based on this idea, more sampling-based methods, e.g., [8], are proposed to improve the optimality of partial trajectories. The idea of PMP is also adopted by some searching-based algorithms [9]- [12], which combines a long-horizon path searcher for improving the global optimality and a local collision avoidance strategy to ensure safety.…”

Section: Introductionmentioning

confidence: 99%

Search-Based Online Trajectory Planning for Car-like Robots in Highly Dynamic Environments

Lin¹,

Zhou²,

Zhu³

et al. 2020

Preprint

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This paper presents a search-based partial motion planner to generate dynamically feasible trajectories for car-like robots in highly dynamic environments. The planner searches for smooth, safe, and near-time-optimal trajectories by exploring a state graph built on motion primitives, which are generated by discretizing the time dimension and the control space. To enable fast online planning, we first propose an efficient path searching algorithm based on the aggregation and pruning of motion primitives. We then propose a fast collision checking algorithm that takes into account the motions of moving obstacles. The algorithm linearizes relative motions between the robot and obstacles and then checks collisions by comparing a point-line distance. Benefiting from the fast searching and collision checking algorithms, the planner can effectively and safely explore the state-time space to generate near-time-optimal solutions. The results through extensive experiments show that the proposed method can generate feasible trajectories within milliseconds while maintaining a higher success rate than up-to-date methods, which significantly demonstrates its advantages.

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Dynamic risk tolerance: Motion planning by balancing short-term and long-term stochastic dynamic predictions

Cited by 16 publications

References 24 publications

Autonomous Flights in Dynamic Environments with Onboard Vision

Autonomous Flights in Dynamic Environments with Onboard Vision

Collision Avoidance of High-Speed Obstacles for Mobile Robots via Maximum-Speed Aware Velocity Obstacle Method

Search-Based Online Trajectory Planning for Car-like Robots in Highly Dynamic Environments

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