Ensemble Metropolis Light Transport

This article proposes a Markov Chain Monte Carlo ( MCMC ) rendering algorithm based on a family of guided transition kernels. The kernels exploit properties of ensembles of light transport paths, which are distributed according to the lighting in the scene, and utilize this information to make informed decisions for guiding local path sampling. Critically, our approach does not require caching distributions in world space, saving time and memory, yet it is able to make guided sampling decisions based on whole paths. We show how this can be implemented efficiently by organizing the paths in each ensemble and designing transition kernels for MCMC rendering based on a carefully chosen subset of paths from the ensemble. This algorithm is easy to parallelize and leads to improvements in variance when rendering a variety of scenes.

Real-Time LIDAR-Based Urban Road and Sidewalk Detection for Autonomous Vehicles

Road and sidewalk detection in urban scenarios is a challenging task because of the road imperfections and high sensor data bandwidth. Traditional free space and ground filter algorithms are not sensitive enough for small height differences. Camera-based or sensor-fusion solutions are widely used to classify drivable road from sidewalk or pavement. A LIDAR sensor contains all the necessary information from which the feature extraction can be done. Therefore, this paper focuses on LIDAR-based feature extraction. For road and sidewalk detection, the current paper presents a real-time (20 Hz+) solution. This solution can also be used for local path planning. Sidewalk edge detection is the combination of three algorithms working parallelly. To validate the result, the de facto standard benchmark dataset, KITTI, was used alongside our measurements. The data and the source code to reproduce the results are shared publicly on our GitHub repository.

An Improved Timed Elastic Band (TEB) Algorithm of Autonomous Ground Vehicle (AGV) in Complex Environment

In recent decades, the Timed Elastic Band (TEB) algorithm is widely used for the AGV local path panning because of its convenient and efficiency. However, it may make a local detour when encountering a curve turn and cause excessive energy consumption. To solve this problem, this paper proposed an improved TEB algorithm to make the AGV walk along the wall when turning, which shortens the planning time and saves energy. Experiments were implemented in the Rviz visualization tool platform of the robot operating system (ROS). Simulated experiment results reflect that an amount of 5% reduction in the planning time has been achieved and the velocity curve implies that the operation was relatively smooth. Practical experiment results demonstrate the effectiveness and feasibility of the proposed method that the robots can avoid obstacles smoothly in the unknown static and dynamic obstacle environment.

Path Planning for Automatic Guided Vehicles (AGVs) Fusing MH-RRT with Improved TEB

In this paper, an AGV path planning method fusing multiple heuristics rapidly exploring random tree (MH-RRT) with an improved two-step Timed Elastic Band (TEB) is proposed. The modified RRT integrating multiple heuristics can search a safer, optimal and faster converge global path within a short time, and the improved TEB can optimize both path smoothness and path length. The method is composed of a global path planning procedure and a local path planning procedure, and the Receding Horizon Planning (RHP) strategy is adopted to fuse these two modules. Firstly, the MH-RRT is utilized to generate a state tree structure as prior knowledge, as well as the global path. Then, a receding horizon window is established to select the local goal point. On this basis, an improved two-step TEB is designed to optimize the local path if the current global path is feasible. Various simulations both on static and dynamic environments are conducted to clarify the performance of the proposed MH-RRT and the improved two-step TEB. Furthermore, real applicative experiments verified the effectiveness of the proposed approach.

E3MoP: Efficient Motion Planning Based on Heuristic-Guided Motion Primitives Pruning and Path Optimization With Sparse-Banded Structure

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 time-optimal 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 B\'{e}zier curve-based state space sampling approach. We name the proposed motion planning framework E$ \mathbf{^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.