Online motion planning for autonomous vehicles in vast environments

Abstract

Nowadays, the potential of autonomous vehicles for order picking and material transport in vast environments with large amounts of obstacles is only exploited to a limited extent. In order to realize free, time-optimal motion of autonomous vehicles through such complex environments, this paper presents a novel motion planning approach. The approach combines a global path planner with a local trajectory generator. The global planner finds a path through the complete environment, taking only the stationary obstacles into account. The local trajectory generator computes a detailed trajectory in a local frame around the global path, accounting for both stationary and moving obstacles. This trajectory is parameterized as a spline, and is obtained by solving an optimal control problem. In order to always include the latest information about the environment, the optimal control problem is solved online with a receding horizon. The paper demonstrates the potential of the proposed method with extensive numerical simulations. In addition, it presents an experimental validation in which a KUKA youBot moves through an obstructed environment. To facilitate the numerical and experimental validation of the presented method, it is embodied in a user-friendly open-source software toolbox.

An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

“…Compared to PSO method, the results showed better optimality for the proposed method. Another hybrid method that includes BSpline is presented in [298]. The authors combined a global path planner with BSpline to achieve free and time-optimal motion of mobile robots in complex environments.…”

Behavior adaptation for mobile robots via semantic map compositions of constraint-based controllers

“…Core behaviors of mobile robots are to reach a target in a map ( Moravec and Elfes, 1985 ), not to collide with occupied areas while driving to the target ( Elfes, 1989 ; LaValle and Kuffner Jr, 2001 ; Marder-Eppstein et al, 2010 ; Mercy et al, 2018 ; Macenski et al, 2020 ), and to adapt the driving behavior to safety and inter-robot coordination constraints ( Wilde et al, 2018 ). A real-world example of such constraints is the traffic system: traffic signs and markings regulate, warn, or guide all traffic participants ( DoT, 2009 ).…”

“…For autonomous robots, such adaptations have been introduced in several ways ( Kuipers, 2000 ): adapting to geometric characteristics of the environment ( Caloud et al, 1990 ; Asama et al, 1991 ; Kato et al, 1992 ), relating behavioral restrictions to areas for the coordination of multi-robot passages, e.g., “not to enter an intersection if something else is present” ( Kim et al, 2016 ; Wilde et al, 2018 ; Ravankar et al, 2019 ; Wilde et al, 2020 ), and adding areas that increase cost functions in path planners ( Hart et al, 1968 ). In principle, all of that mentioned above can be incorporated into a COP, more in particular, of the Model Predictive Control (MPC) variety ( Mayne et al, 2000 ; Mercy et al, 2018 ).…”

“…Hence, no claims are made regarding superiority in quantitative measures as time or distance minimization. Control engineers on their own will most likely design a more optimal COP by specifically parameterizing it to the used COP solver and environment ( Mercy et al, 2018 ). Section 6 provides a conclusion and discussion of the methodology.…”

Multi-stage Optimal Control Problem Formulation for Drone Racing Through Gates and Tunnels

“…With the drone dynamics and the representation of gates in place, the control problem can be formulated. For one gate, the time-optimal control problem is split up into three stages, similarly to the multi-frame approach by Mercy et al for traveling through vast environments [17]: an approach stage, a fly-through stage and a fly-away stage. The drone position is subject to stage-specific box constraints, determined by the relative location with respect to the gate opening.…”