E ³ MoP: Efficient Motion Planning Based on Heuristic-Guided Motion Primitives Pruning and Path Optimization With Sparse-Banded Structure

Abstract

To solve the autonomous navigation problem in complex environments, an efficient motion planning approach is newly presented in this paper. Considering the challenges from large-scale, partially unknown complex environments, a three-layer motion planning framework is elaborately designed, including global path planning, local path optimization, and timeoptimal velocity planning. Compared with existing approaches, the novelty of this work is twofold: 1) a novel heuristic-guided pruning strategy of motion primitives is proposed and fully integrated into the state lattice-based global path planner to further improve the computational efficiency of graph search, and 2) a new soft-constrained local path optimization approach is proposed, wherein the sparse-banded system structure of the underlying optimization problem is fully exploited to efficiently solve the problem. We validate the safety, smoothness, flexibility, and efficiency of our approach in various complex simulation scenarios and challenging real-world tasks. It is shown that the computational efficiency is improved by 66.21% in the global planning stage and the motion efficiency of the robot is improved by 22.87% compared with the recent quintic Bézier curve-based state space sampling approach. We name the proposed motion planning framework E 3 MoP, where the number 3 not only means our approach is a three-layer framework but also means the proposed approach is efficient in three stages.Note to Practitioners-This paper is motivated by the challenges of motion planning problems of mobile robots. A threelayer motion planning framework is proposed by combining global path planning, local path optimization, and time-optimal velocity planning. For mobile robot navigation applications in semi-structured environments, optimization-based local planners are recommended. Extensive simulation and experimental results show the effectiveness of the proposed motion planning framework. However, due to the non-convexity of the path optimization formulation, the proposed local planner may get stuck in local optima. In future research, we will concentrate on extending the proposed local path optimization approach with the theory of homology classes to maintain several homotopically distinct local paths and seek global optima.

G$ \mathbf{^2} $VD Planner: An Efficient Motion Planning Approach With Grid-based Generalized Voronoi Diagrams

“…In [14] smooth the path generated by the hybrid A* algorithm. In [15], Wen et al propose a gradient-based local path smoothing approach for mobile robots, wherein the sparse-banded system structure of the underlying optimization problem is fully exploited to efficiently solve the problem. The above soft-constrained approaches utilize gradient information to push the path far from obstacles and can obtain a path with better safety.…”

“…To validate the effectiveness of the proposed QP-based path smoothing approach, we compare it with a recent gradient-based path smoothing approach SBA [15]. We comprehensively evaluate path smoothing approaches in terms of travel distance, efficiency, and smoothness.…”

BA-LIOM: tightly coupled laser-inertial odometry and mapping with bundle adjustment

“…The research emphasis regarding state estimation and mapping of mobile robots has revolved around achieving high levels of accuracy and real-time performance. Accurate pose estimation and precise map can ensure the function of closed-loop control, autonomous exploration, obstacle avoidance, and motion planning [1]. Currently, the perception of robots relies on lidar or camera or a combination of the two.…”

Position Checking-Based Sampling Approach Combined with Attraction Point Local Optimization for Safe Flight of UAVs

“…A collision-free, smooth, and dynamically feasible trajectory guarantees flight safety, which is generated by a motion planning module in working scenarios. Motion planning is generally divided into two parts: front-end discrete path search and back-end continuous trajectory optimization, aiming at generating a reference trajectory that satisfies the above three basic conditions for the controller to track [3,4]. Since quadrotor UAVs with small fuselages have limited computational resources of onboard computers, a major research objective is how to fully utilize the limited resources to generate front-end paths and back-end trajectories.…”