Interleaving Graph Search and Trajectory Optimization for Aggressive Quadrotor Flight

Natarajan, Ramkumar; Choset, Howie; Likhachev, Maxim

doi:10.1109/lra.2021.3067298

Cited by 19 publications

(6 citation statements)

References 70 publications

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“…Hybrid approaches combine two or more ideas. One can combine search and optimization [19], search and sampling [20], [21], [22], or combine sampling and optimization [23]. For some dynamical systems, using insights from control theory for the motion planning can also be beneficial [24], [25], but requires domain knowledge.…”

Section: Related Workmentioning

confidence: 99%

db-A*: Discontinuity-bounded Search for Kinodynamic Mobile Robot Motion Planning

Hoenig¹,

Ortiz-Haro²,

Toussaint³

2022

Preprint

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We consider time-optimal motion planning for dynamical systems that are translation-invariant, a property that holds for many mobile robots, such as differential-drives, cars, airplanes, and multirotors. Our key insight is that we can extend graph-search algorithms to the continuous case when used symbiotically with optimization. For the graph search, we introduce discontinuity-bounded A* (db-A*), a generalization of the A* algorithm that uses concepts and data structures from sampling-based planners. Db-A* reuses short trajectories, so-called motion primitives, as edges and allows a maximum user-specified discontinuity at the vertices. These trajectories are locally repaired with trajectory optimization, which also provides new improved motion primitives. Our novel kinodynamic motion planner, kMP-db-A*, has almost surely asymptotic optimal behavior and computes near-optimal solutions quickly. For our empirical validation, we provide the first benchmark that compares search-, sampling-, and optimizationbased time-optimal motion planning on multiple dynamical systems in different settings. Compared to the baselines, kMPdb-A* consistently solves more problem instances, finds lowercost initial solutions, and converges more quickly.A) δ = 0.41 T ∆t = 13.3s B) δ = 0.22 T ∆t = 16.3s C) δ = 0.00 T ∆t = 19.5s

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Section: Related Workmentioning

confidence: 99%

db-A*: Discontinuity-bounded Search for Kinodynamic Mobile Robot Motion Planning

Hoenig¹,

Ortiz-Haro²,

Toussaint³

2022

Preprint

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“…• A novel adaptation of INSAT: INterleaved Search And Trajectory optimization [1] for the application of torquelimited manipulation planning through contact. By interleaving discrete graph-search with continuous trajectory optimization, our algorithm is able to plan through contact over long horizons for high-dimensional complex manipulation problems in confined non-convex environments.…”

Section: Imentioning

confidence: 99%

“…4). We will then explain how INSAT [1] is adapted for the application of torque-limited manipulation planning with contact. Finally, we provide experimental evidence (Sec.…”

Section: T -L P W Cmentioning

confidence: 99%

Torque-Limited Manipulation Planning through Contact by Interleaving Graph Search and Trajectory Optimization

Natarajan¹,

Johnston²,

Simaan³

et al. 2022

Preprint

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Robots often have to perform manipulation tasks in close proximity to people (Fig 1). As such, it is desirable to use a robot arm that has limited joint torques so as to not injure the nearby person. Unfortunately, these limited torques then limit the payload capability of the arm. By using contact with the environment, robots can expand their reachable workspace that, otherwise, would be inaccessible due to exceeding actuator torque limits. We adapt our recently developed INSAT algorithm [1] to tackle the problem of torque-limited whole arm manipulation planning through contact. INSAT requires no prior over contact mode sequence and no initial template or seed for trajectory optimization. INSAT achieves this by interleaving graph search to explore the manipulator joint configuration space with incremental trajectory optimizations seeded by neighborhood solutions to find a dynamically feasible trajectory through contact. We demonstrate our results on a variety of manipulators and scenarios in simulation. We also experimentally show our planner exploiting robot-environment contact for the pick and place of a payload using a Kinova Gen3 robot. In comparison to the same trajectory running in free space, we experimentally show that the utilization of bracing contacts reduces the overall torque required to execute the trajectory.

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“…In addition, even using asymptotically-optimal sampling-based planners [17,11,16], the trajectories we design can be considerably suboptimal in practice, where only a finite number of samples can be taken. For certain classes of dynamical systems, hybrid approaches, where a trajectory-optimization planner is driven by a higher-level graph search, have been shown to overcome part of these difficulties [29]. Still, these multi-layer architectures do not offer a unified formulation of the planning problem as a single optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Motion Planning around Obstacles with Convex Optimization

Marcucci¹,

Petersen²,

Wrangel³

et al. 2022

Preprint

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Trajectory optimization offers mature tools for motion planning in high-dimensional spaces under dynamic constraints. However, when facing complex configuration spaces, cluttered with obstacles, roboticists typically fall back to sampling-based planners that struggle in very high dimensions and with continuous differential constraints. Indeed, obstacles are the source of many textbook examples of problematic nonconvexities in the trajectory-optimization problem. Here we show that convex optimization can, in fact, be used to reliably plan trajectories around obstacles. Specifically, we consider planning problems with collision-avoidance constraints, as well as cost penalties and hard constraints on the shape, the duration, and the velocity of the trajectory. Combining the properties of Bézier curves with a recently-proposed framework for finding shortest paths in Graphs of Convex Sets (GCS), we formulate the planning problem as a compact mixed-integer optimization. In stark contrast with existing mixed-integer planners, the convex relaxation of our programs is very tight, and a cheap rounding of its solution is typically sufficient to design globally-optimal trajectories. This reduces the mixed-integer program back to a simple convex optimization, and automatically provides optimality bounds for the planned trajectories. We name the proposed planner GCS, after its underlying optimization framework. We demonstrate GCS in simulation on a variety of robotic platforms, including a quadrotor flying through buildings and a dual-arm manipulator (with fourteen degrees of freedom) moving in a confined space. Using numerical experiments on a seven-degree-of-freedom manipulator, we show that GCS can outperform widely-used sampling-based planners by finding higher-quality trajectories in less time.

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Interleaving Graph Search and Trajectory Optimization for Aggressive Quadrotor Flight

Cited by 19 publications

References 70 publications

db-A*: Discontinuity-bounded Search for Kinodynamic Mobile Robot Motion Planning

db-A*: Discontinuity-bounded Search for Kinodynamic Mobile Robot Motion Planning

Torque-Limited Manipulation Planning through Contact by Interleaving Graph Search and Trajectory Optimization

Motion Planning around Obstacles with Convex Optimization

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