Safe, Aggressive Quadrotor Flight via Reachability-Based Trajectory Design

Kousik, Shreyas; Holmes, Patrick D.; Vasudevan, Ram

doi:10.1115/dscc2019-9214

Cited by 35 publications

(38 citation statements)

References 23 publications

(94 reference statements)

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“…The purpose of this article is to establish the foundation of RTD as a provably safe and persistently feasible planner. These techniques can also be extended and applied to dynamic environments (Vaskov et al, 2019a), fast robots such as autonomous vehicles (Vaskov et al, 2019b), aerial robots (Kousik et al, 2019), and manipulator arms (Holmes et al, 2020). In summary, the RTD framework can serve as a useful lens to address the challenges of safe, real-time robot trajectory design.…”

Section: Discussionmentioning

confidence: 99%

“…This type of decomposition applies when the robot’s dynamic model can be split into subsystems of lower dimension. For example, the Segway and Rover models presented in this work, or the quadrotor models presented in Chen et al (2016) and Kousik et al (2019). Note that the recovery of the exact FRS is not always possible with this approach.…”

Section: System Decompositionmentioning

confidence: 99%

“…There are several ways to satisfy Assumption 35. One way is to directly include braking maneuvers in the parameterized trajectories (Kousik et al, 2019; Vaskov et al, 2019a). However, this increases the complexity of the offline reachability analysis by either increasing the degree or dimension of the trajectory-producing model, or introducing time-switching dynamics.…”

Section: Conditions For Safety and Persistentfeasibilitymentioning

confidence: 99%

See 2 more Smart Citations

Bridging the gap between safety and real-time performance in receding-horizon trajectory design for mobile robots

Kousik

Vaskov

Fan

et al. 2020

The International Journal of Robotics Research

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To operate with limited sensor horizons in unpredictable environments, autonomous robots use a receding-horizon strategy to plan trajectories, wherein they execute a short plan while creating the next plan. However, creating safe, dynamically feasible trajectories in real time is challenging, and planners must ensure persistent feasibility, meaning a new trajectory is always available before the previous one has finished executing. Existing approaches make a tradeoff between model complexity and planning speed, which can require sacrificing guarantees of safety and dynamic feasibility. This work presents the Reachability-based Trajectory Design (RTD) method for trajectory planning. RTD begins with an offline forward reachable set (FRS) computation of a robot’s motion when tracking parameterized trajectories; the FRS provably bounds tracking error. At runtime, the FRS is used to map obstacles to parameterized trajectories, allowing RTD to select a safe trajectory at every planning iteration. RTD prescribes an obstacle representation to ensure that obstacle constraints can be created and evaluated in real time while maintaining safety. Persistent feasibility is achieved by prescribing a minimum sensor horizon and a minimum duration for the planned trajectories. A system decomposition approach is used to improve the tractability of computing the FRS, allowing RTD to create more complex plans at runtime. RTD is compared in simulation with rapidly-exploring random trees and nonlinear model-predictive control. RTD is also demonstrated in randomly crafted environments on two hardware platforms: a differential-drive Segway and a car-like Rover. The proposed method is safe and persistently feasible across thousands of simulations and dozens of real-world hardware demos.

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Section: Discussionmentioning

confidence: 99%

Section: System Decompositionmentioning

confidence: 99%

Section: Conditions For Safety and Persistentfeasibilitymentioning

confidence: 99%

See 1 more Smart Citation

Bridging the gap between safety and real-time performance in receding-horizon trajectory design for mobile robots

Kousik

Vaskov

Fan

et al. 2020

The International Journal of Robotics Research

Self Cite

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“…Kousik et. al [18] learns a tracking error function offline to conservatively approximate reachable set at runtime, with hardware implementation and time-varying uncertainties as future work. Most similar to our problem space, [19] approximates in real-time the reachable set given current disturbance, while our work precomputes margins for faster adaptation.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Safety Margin Estimation for Safe Real-Time Replanning under Time-Varying Disturbance

Ho,

Patrikar,

Bonatti

et al. 2021

Preprint

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Safe navigation in real time is challenging because engineers need to work with uncertain vehicle dynamics, variable external disturbances, and imperfect controllers. A common safety strategy is to inflate obstacles by hand-defined margins. However, arbitrary static margins often fail in more dynamic scenarios, and using worst-case assumptions is overly conservative for most settings where disturbances over time. In this work, we propose a middle ground: safety margins that adapt on-the-fly. In an offline phase, we use Monte Carlo simulations to pre-compute a library of safety margins for multiple levels of disturbance uncertainties. Then, at runtime, our system estimates the current disturbance level to query the associated safety margins that best trades off safety and performance. We validate our approach with extensive simulated and real-world flight tests. We show that our adaptive method significantly outperforms static margins, allowing the vehicle to operate up to 1.5 times faster than worstcase static margins while maintaining safety. Video: https://youtu.be/SHzKHSUjdUU

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“…There have been attempts to model the error in tracking trajectories. The work presented in [24] modelled the error dynamics as a separate LTI system while, in [29], a Reachable set was computed offline based on motion primitives. The bounds on tracking based on uncertain speed profiles was used in [19].…”

Section: B Accounting Trajectory Tracking Errormentioning

confidence: 99%

Scalable Decentralized Multi-Robot Trajectory Optimization in Continuous-Time

2020

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This paper presents a decentralized algorithm that generates continuous-time trajectory online for a swarm of robots based upon model predictive control. To generate collision-free trajectory, temporally distinct safe regions are formed such that the robots are confined to move within these safe regions to avoid collisions with one another. The distinct safe regions are temporally linked by generating a B-spline. Additionally, to ensure that collisions are avoided, collision-regions that the robots have to stay outside are also generated distinctly. A non linear program (NLP) with an objective to make the robots stay outside the collision-regions and stay within the safe regions is formulated. The algorithm was tested in simulations on Gazebo with aerial robots. The simulated results suggest that the proposed algorithm is computationally efficient and can be used for online planning in moderate sized multi-robot systems.

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Safe, Aggressive Quadrotor Flight via Reachability-Based Trajectory Design

Cited by 35 publications

References 23 publications

Bridging the gap between safety and real-time performance in receding-horizon trajectory design for mobile robots

Bridging the gap between safety and real-time performance in receding-horizon trajectory design for mobile robots

Adaptive Safety Margin Estimation for Safe Real-Time Replanning under Time-Varying Disturbance

Scalable Decentralized Multi-Robot Trajectory Optimization in Continuous-Time

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