Emergency maneuver library - ensuring safe navigation in partially known environments

Abstract

Autonomous mobile robots are required to operate in partially known and unstructured environments. It is imperative to guarantee safety of such systems for their successful deployment. Current state of the art does not fully exploit the sensor and dynamic capabilities of a robot. Also, given the non-holonomic systems with non-linear dynamic constraints, it becomes computationally infeasible to find an optimal solution if the full dynamics are to be exploited online. In this paper we present an online algorithm to guarantee the safety of the robot through an emergency maneuver library. The maneuvers in the emergency maneuver library are optimized such that the probability of finding an emergency maneuver that lies in the known obstacle free space is maximized. We prove that the related trajectory set diversity problem is monotonic and submodular which enables one to develop an efficient trajectory set generation algorithm with bounded sub-optimality. We generate an off-line computed trajectory set that exploits the full dynamics of the robot and the known obstacle-free region. We test and validate the algorithm on a full-size autonomous helicopter flying up to speeds of 56 m /s in partially-known environments. We present results from 4 months of flight testing where the helicopter has been avoiding trees, performing autonomous landing, avoiding mountains while being guaranteed safe.

On Infusing Reachability-Based Safety Assurance Within Probabilistic Planning Frameworks for Human-Robot Vehicle Interactions

“…This represents a compromise between model fidelity and the number of state dimensions in the relative dynamics; reducing the latter is essential since solving the HJI PDE suffers greatly from the curse of dimensionality. 3 We define the relative dynamics (4) in a coordinate system centered on and aligned with the robot, where R θ is the counterclockwise rotation matrix over θ .…”

“…We call the Operator Splitting Quadratic Program (OSQP) solver [18] through the Parametron.jl modeling framework [19]; this combination of software enables us to solve the following MPC optimization problem at 100Hz. 3 The computation becomes notoriously expensive past five or more states without compromising on grid discretization or employing some decoupling strategy. We use a grid size of 13 × 13 × 9 × 9 × 9 × 9 × 9 for our 7D system uniformly spaced over ( 1]; computing the BRS with this discretization takes approximately 16 hours on a 3.6GHz octocore AMD Ryzen 1800X CPU.…”

“…Safety is a function of uncertainty in both the robot's dynamics and those of its surroundings; high-level planners typically do not replan sufficiently rapidly to ensure split-second reactivity to threats, yet low-level controllers are typically too short-sighted to ensure safety beyond their local horizon. Hand-designed artificial potential fields have been employed at the planning level [1] to encourage the selection of safer nominal trajectories, while reactive collision avoidance techniques, e.g., based on online optimal control [2] or precomputed emergency maneuver libraries [3], have been applied at the controller level to avoid previously unseen static obstacles, but these approaches do not account for interactive scenarios in which another sentient agent is a key environmental consideration.…”

High performance and safe flight of full‐scale helicopters from takeoff to landing with an ensemble of planners

“…Safety is ensured by two submodules—safety checker and evasive maneuver library (Figure ). The library consists of a diverse set of evasive maneuvers that fulfill the definition of safety as described in , and whose construction is detailed in Arora et al (). The safety checker is a submodule whose purpose is to ensure that the vehicle stays in known, safe space, by consistently checking the safety of the last planned trajectory and stitching maneuvers from the aforementioned library to it.…”

“…This paper builds upon our previous works on most of the individual components of our framework: using an ensemble of planners (Choudhury, Arora, & Scherer, , ), dynamics projection filter (Choudhury & Scherer, ), guaranteeing safety (Arora, Choudhury, Althoff, & Scherer, , ), training a dynamic list of planners (Tallavajhula & Choudhury, ), and smooth route optimization (Dugar, Choudhury, & Scherer, , 2017b). In this paper, for the first time we present a description of the unified architecture, details of the trajectory planning framework and extensive flight test evaluation of the whole system.…”