Exact and Bounded Collision Probability for Motion Planning under Gaussian Uncertainty
Computing collision-free trajectories is of prime importance for safe navigation. We present an approach for computing the collision probability under Gaussian distributed motion and sensing uncertainty with the robot and static obstacle shapes approximated as ellipsoids. The collision condition is formulated as the distance between ellipsoids and unlike previous approaches we provide a method for computing the exact collision probability. Furthermore, we provide a tight upper bound that can be computed much faster during online planning. Comparison to other state-of-the-art methods is also provided. The proposed method is evaluated in simulation under varying configuration and number of obstacles.
Code (0)
등록된 구현이 없습니다.
Tasks
Motion PlanningSimilar Papers 제목 키워드 기반
Safe motion planning with environment uncertainty
We present an approach for safe motion planning under robot state and environment (obstacle and landmark location) uncertainties. To this end, we first develop an approach that accounts for the landmark uncertainties dur…
Motion PlanningVector Field-based Collision Avoidance for Moving Obstacles with Time-Varying Elliptical Shape
This paper presents an algorithm for local motion planning in environments populated by moving elliptical obstacles whose velocity, shape and size are fully known but may change with time. We base the algorithm on a coll…
Collision AvoidanceMotion PlanningNavigateSemidefinite Relaxations for Collision-Free Motion Planning
We study semidefinite relaxations for collision-free motion planning. We focus on a point robot moving from start to goal through spherical obstacles in $\mathbb{R}^n$, subject to path continuity constraints and squared …
Motion PlanningSafe and Non-Conservative Trajectory Planning for Autonomous Driving Handling Unanticipated Behaviors of Traffic Participants
Trajectory planning for autonomous driving is challenging because the unknown future motion of traffic participants must be accounted for, yielding large uncertainty. Stochastic Model Predictive Control (SMPC)-based plan…
Autonomous DrivingModel Predictive ControlTrajectory PlanningSpace-Time Graphs of Convex Sets for Multi-Robot Motion Planning
We address the Multi-Robot Motion Planning (MRMP) problem of computing collision-free trajectories for multiple robots in shared continuous environments. While existing frameworks effectively decompose MRMP into single-r…
Motion Planning