A Modular Framework for Motion Planning Using Safe-by-Design Motion Primitives

Abstract

We present a modular framework for solving a motion planning problem among a group of robots. The proposed framework utilizes a finite set of low level motion primitives to generate motions in a gridded workspace. The constraints on allowable sequences of motion primitives are formalized through a maneuver automaton. At the high level, a control policy determines which motion primitive is executed in each box of the gridded workspace. We state general conditions on motion primitives to obtain provably correct behavior so that a library of safe-by-design motion primitives can be designed. The overall framework yields a highly robust design by utilizing feedback strategies at both the low and high levels. We provide specific designs for motion primitives and control policies suitable for multi-robot motion planning; the modularity of our approach enables one to independently customize the designs of each of these components. Our approach is experimentally validated on a group of quadrocopters.

Two-timescale Mechanism-and-Data-Driven Control for Aggressive Driving of Autonomous Cars

“…The idea of using motion primitives to relieve the planner of the computational burden stemming from system dynamics and nonholonomic constraints was originated and developed by Frazzoli et al [18], [19]. The aforementioned framework has been applied to quadrotor [20] and fixed-wing [8] aircraft, legged robots [21] and autonomous driving [22]. To our knowledge, the application of primitive-based planning to aggressive driving scenarios has not been widely studied, due in part to its sensitivity to model mismatch, exacerbated by tire saturation, which the present work addresses via the coupling of primitive-based planning and data-driven control.…”

Invariant Set Distributed Explicit Reference Governors for Provably Safe On-Board Control of Nano-Quadrotor Swarms

“…Most approaches, such as (Campos-Macías et al, 2017;Chen et al, 2017;Herbert et al, 2017;Preiss et al, 2017a;Wang et al, 2017;Fridovich-Keil et al, 2018;Honig et al, 2018;Kolaric et al, 2018;Cappo et al, 2018a;Cappo et al, 2018b;Xu and Sreenath, 2018;Bajcsy et al, 2019;Du et al, 2019;Fathian et al, 2019;Liu et al, 2019;Luis and Schoellig, 2019;Rubies-Royo et al, 2019;Vukosavljev et al, 2019), try to ensure a particular level of safety and robustness, by running the core search-based or optimization-based algorithms off-board the UAVs, and thus outsource the high computational cost to ground control stations that send the trajectories to the UAV's on-board position or attitude controller. Frameworks such as (Preiss et al, 2017a;Honig et al, 2018) combine graph-based planning and continuous trajectory optimization to compute safe and smooth trajectories, but take several minutes for a swarm of hundreds of quadrotors in obstacle-rich environments.…”

“…For a single nano-quadrotor in obstacle-dense environments, a provably safe trajectory can be computed online every 0.1-1s, depending on the scenario. Frameworks such as (Du et al, 2019;Vukosavljev et al, 2019) are based on designing off-board libraries of safe motion primitives for a swarm of tiny MAVs, but typically require too much memory for on-board implementation. (Du et al, 2019) relies on combinatorial and nonlinear optimization techniques that are executed on a central computer, requires iterative procedures to resolve collisions between agents in a sequential manner, and does not guarantee to find a feasible solution.…”

“…(Du et al, 2019) relies on combinatorial and nonlinear optimization techniques that are executed on a central computer, requires iterative procedures to resolve collisions between agents in a sequential manner, and does not guarantee to find a feasible solution. A modular, robust, and hierarchical framework for safe planning of robot teams is proposed in (Vukosavljev et al, 2019). Although the runtime components, executed off-board, require only a small computing time, this approach is centralized, requires a-priori known environments and is conservative due to the restriction to a discretization, i.e.…”

“…In these experiments the UAVs are modeled as cylinders as detailed in Section 4.2.4, preventing them to fly over each other. Similarly to (Preiss et al, 2017a;Honig et al, 2018;Vukosavljev et al, 2019), this choice prevents a MAV's propeller downwash effect to destabilize other MAVs which are flying closely underneath.…”