Motion Planning Framework Research Articles

A Data-Driven Method Towards Minimizing Collision Severity for Highly Automated Vehicles

The deployment of autonomous vehicles on public roads calls for the development of methods that are reliably able to mitigate injury severity in case of unavoidable collisions. This study proposes a data-driven motion planning method capable of minimizing injury severity for vehicle occupants in unavoidable collisions. The method is based on establishing a metric that models the relationship between impact location and injury severity using real accident data, and subsequently including it in the cost function of a motion planning framework. The vehicle dynamics and associated constraints are considered through a precomputed trajectory library, which is generated by solving an optimal control problem. This allows for efficient computation as well as an accurate representation of the vehicle. The proposed motion planning approach is evaluated by simulation, and it is shown that the trajectory associated with the minimum cost mitigates the collision severity for occupants of passenger vehicles involved in the collision.

Structured road-oriented motion planning and tracking framework for active collision avoidance of autonomous vehicles

Structured road-oriented motion planning and tracking framework for active collision avoidance of autonomous vehicles

Autonomous Bus Driving: A Novel Motion-Planning Approach

In this article, we present a motion-planning framework that leverages expert bus driver behavior, increasing the safety and maneuverability of autonomous buses. Autonomous vehicles will increase the safety, quality, and efficiency of transportation systems. However, to deploy this technology in urban public transport, many challenges related to self-driving buses still need to be addressed. Unlike passenger cars, buses have long and wide dimensions and a distinct chassis configuration, which significantly challenges their maneuverability. To deal with their special dimensions, we introduce a novel optimization objective that centers their whole body as they travel along a road. Furthermore, we present new environment classification schemes that enable self-driving buses to take advantage of their distinct chassis configuration, namely, the elevated overhangs, to increase maneuverability. Finally, we offer a novel collision checking method that explicitly considers a bus's front wheels and how they can protrude from beneath the chassis when maneuvering near stops. We demonstrate the benefits of our framework through experiments using an autonomous bus in real road scenarios.

Predicted Composite Signed-Distance Fields for Real-Time Motion Planning in Dynamic Environments

We present a novel framework for motion planning in dynamic environments that accounts for the predicted trajectories of moving objects. We explore the use of composite signed-distance fields in motion planning and detail how they can be used to generate signed-distance fields (SDFs) in real-time to incorporate predicted obstacle motions; to achieve this, we introduce the concept of predicted signed-distance fields. We benchmark our approach of using composite SDFs against performing exact SDF calculations on the workspace occupancy grid. Our proposed technique generates predictions substantially faster and typically exhibits an 81-97% reduction in time for subsequent predictions. We integrate our framework with GPMP2 to demonstrate a full implementation of our approach in real-time, enabling a 7-DoF Panda manipulator to smoothly avoid a moving obstacle in simulation and hardware experiments.

Autonomous Driving Ethics: from Trolley Problem to Ethics of Risk

In 2017, the German ethics commission for automated and connected driving released 20 ethical guidelines for autonomous vehicles. It is now up to the research and industrial sectors to enhance the development of autonomous vehicles based on such guidelines. In the current state of the art, we find studies on how ethical theories can be integrated. To the best of the authors’ knowledge, no framework for motion planning has yet been published which allows for the true implementation of any practical ethical policies. This paper makes four contributions: Firstly, we briefly present the state of the art based on recent works concerning unavoidable accidents of autonomous vehicles (AVs) and identify further need for research. While most of the research focuses on decision strategies in moral dilemmas or crash optimization, we aim to develop an ethical trajectory planning for all situations on public roads. Secondly, we discuss several ethical theories and argue for the adoption of the theory “ethics of risk.” Thirdly, we propose a new framework for trajectory planning, with uncertainties and an assessment of risks. In this framework, we transform ethical specifications into mathematical equations and thus create the basis for the programming of an ethical trajectory. We present a risk cost function for trajectory planning that considers minimization of the overall risk, priority for the worst-off and equal treatment of people. Finally, we build a connection between the widely discussed trolley problem and our proposed framework.

A Survey on Novel Estimation Approach of Motion Controllers for Self-Driving Cars

The motion planning framework is one of the challenging tasks in autonomous driving cars. During motion planning, predicting of trajectory is computed by Gaussian propagation. Recently, the localization uncertainty control will be estimating by Gaussian framework. This estimation suffers from real time constraint distribution for (Global Positioning System) GPS error. In this research article compared novel motion planning methods and concluding the suitable estimating algorithm depends on the two different real time traffic conditions. One is the realistic unusual traffic and complex target is another one. The real time platform is used to measure the several estimation methods for motion planning. Our research article is that comparing novel estimation methods in two different real time environments and an identifying better estimation method for that. Our suggesting idea is that the autonomous vehicle uncertainty control is estimating by modified version of action based coarse trajectory planning. Our suggesting framework permits the planner to avoid complex and unusual traffic (uncertainty condition) efficiently. Our proposed case studies offer to choose effectiveness framework for complex mode of surrounding environment.

Benchmarking framework for robotic arc welding motion planning

Producing a small series of large complex parts using robotic arc welding is challenging due to the time it takes to program the robot. Currently, offline programming software assists the operator in programming hundreds of welds. This is often still an iterative process because the welding robot or torch may be in collision during part of the weld motion. Adding motion planning software in this workflow could reduce the time required to generate a robot program. However, the capabilities of motion planning algorithms in this context are not clear. We present a generic framework to evaluate and compare motion planning algorithms for robot arc welding. This framework allows integration of existing open source and commercial planners on different levels of abstraction depending on their capabilities. Different planners can be combined and benchmarked as a group if a single planner is not sufficient to solve all aspects of the problem. The framework is applied to two realistic welding cases to show its potential.

Target Vehicle Motion Prediction-Based Motion Planning Framework for Autonomous Driving in Uncontrolled Intersections

This paper presents a motion-planning framework for urban autonomous driving at uncontrolled intersections. The intention and future state of the target vehicles are predicted using information obtained from the environment sensors. The target state prediction module employs an Interacting Multiple Model (IMM) filter to infer the intention of targets. The prediction results of each model of the IMM filter are fused to predict the future state of targets. The proposed predictor uses the intelligent driver model based-driver behavior model to construct the local filter of IMM. The driving mode decision is realized as a state machine consisting of two phases, ‘Approach’ and ‘Risk Management’. The risk management phase is composed of two sub-modes, ‘Cross’ and ‘Yield’. The state transition conditions between phases and modes are defined by introducing the concepts of ‘Critical gap’ and ‘Follow-up gap’. Based on the determined driving mode, the motion planning module consists of two sub-modules for each phase. The required deceleration determination for the approach phase is proposed to consider the occluded region in order to prevent inevitable collisions caused by fast approaches. The model predictive controller for the risk management phase is designed to determine the desired acceleration to guarantee safety and prevent unnecessary deceleration simultaneously. Both computer simulation studies and vehicle tests are conducted to evaluate the proposed framework. The results indicate that the proposed framework ensures the safety at uncontrolled intersections with a driving pattern similar to that of a driver.

Adaptively Robust Control Policy Synthesis Through Riemannian Motion Policies

Riemannian Motion Policies (RMPs) have recently been introduced as an online motion planning and policy synthesis framework that designs second-order motion policies defined on robot task spaces, and combines them into one global policy trading off between various motion objectives. Until now RMPs have only been applied either through direct joint-space acceleration control or inverse dynamics control assuming perfect knowledge of the system dynamics. It is unclear how RMPs can be implemented in the presence of dynamics modeling uncertainty and external disturbance. We address this by augmenting the existing RMP framework with a novel robust control Lyapunov function (RCLF) based inverse dynamics controller. This combination produces a fast, reactive, online motion planning and control framework that is also robust to system dynamics parameter uncertainty and external disturbance. We also propose a robust gain adaptation law that can automatically compensate for parameter uncertainty and external disturbance. We provide stability guarantees for the proposed RCLF controller with the gain adaptation law. Additionally, performance of the combined RMP-RCLF system is demonstrated on a 7-degree of freedom (DoF) robot manipulator arm in simulation.

Occlusion-Aware Motion Planning at Roundabouts

In this work, we present a motion planning framework for automated vehicles to drive safely through intersections despite occlusions and the uncertain behavior of the surrounding vehicles. A context representation based on probably-free gaps is proposed as a means to provide, in occluded scenes, richer information to the motion planner compared to representations only based on observed objects. Our solution builds upon the path-velocity decomposition approach. Path planning is performed with state-of-the-art techniques, while a novel trajectory abstraction is used to reason about speed profiles without explicitly generating sequences of accelerations. To efficiently identify the best safe speed profile for every path candidate, a reachability-based analysis is also formulated. The proposed planning workflow is evaluated in roundabout scenarios. Our simulation study shows that the proposed context representation facilitates the decision-making in occluded scenes and that the reachability-based planning strategy is robust, computationally efficient, and outperforms a simpler reactive strategy.

A Motion Planning Approach for Nonprehensile Manipulation and Locomotion Tasks of a Legged Robot

Nonprehensile manipulation produces underconstraint motions that are sensitive to environmental dynamics. Legged locomotion constitutes a floating-based movement, whose dynamic is underactuated with respect to the inertial frame. When these two tasks are combined, system motion planning and control are complex due to their inherent underactuated features. This article presents a motion planning framework for a legged robot that uses its limbs for nonprehensile manipulation, as well as locomoting motions. First, issues related to the description of the robot–object–environment system and the task are presented. The velocity constraint that prevents separation and the force constraint that restricts interactive forces are then integrated into the system dynamic model to produce bounds on the system acceleration as a function of the system state. Then, we solve the motion planning problem by reducing the system dimensions in operational space and programming feasible trajectories within the phase plane. This approach is employed to control the quadruped robot TITAN-VIII to manipulate objects and locomote itself using Drive Mode , Inchworm Mode , Scoot Mode , and Throw Mode . Experimental results obtained through simulations and physical tests are reported to demonstrate the effectiveness of our approach.

A Multi-Level Optimization Framework for Simultaneous Grasping and Motion Planning

We present an optimization framework for grasp and motion planning in the context of robotic assembly. Typically, grasping locations are provided by higher level planners or as input parameters. In contrast, our mathematical model simultaneously optimizes motion trajectories, grasping locations, and other parameters such as the pose of an object during handover operations. The input to our framework consists of a set of objects placed in a known configuration, their target locations, and relative timing information describing when objects need to be picked up, optionally handed over, and dropped off. To allow robots to reason about the way in which grasping locations govern optimal motions, we formulate the problem using a multi-level optimization scheme: the top level optimizes grasping locations; the mid-layer level computes the configurations of the robot for pick, drop and handover states; and the bottom level computes optimal, collision-free motions. We leverage sensitivity analysis to compute derivatives analytically (how do grasping parameters affect IK solutions, and how these, in turn, affect motion trajectories etc.), and devise an efficient numerical solver to generate solutions to the resulting optimization problem. We demonstrate the efficacy of our approach on a variety of assembly and handover tasks performed by a dual-armed robot with parallel grippers.

Guided Stochastic Optimization for Motion Planning.

Learning from Demonstration (LfD) is a family of methods used to teach robots specific tasks. It is used to assist them with the increasing difficulty of performing manipulation tasks in a scalable manner. The state-of-the-art in collaborative robots allows for simple LfD approaches that can handle limited parameter changes of a task. These methods however typically approach the problem from a control perspective and therefore are tied to specific robot platforms. In contrast, this paper proposes a novel motion planning approach that combines the benefits of LfD approaches with generic motion planning that can provide robustness to the planning process as well as scaling task learning both in number of tasks and number of robot platforms. Specifically, it introduces Dynamical Movement Primitives (DMPs) based LfD as initial trajectories for the Stochastic Optimization for Motion Planning (STOMP) framework. This allows for successful task execution even when the task parameters and the environment change. Moreover, the proposed approach allows for skill transfer between robots. In this case a task is demonstrated to one robot via kinesthetic teaching and can be successfully executed by a different robot. The proposed approach, coined Guided Stochastic Optimization for Motion Planning (GSTOMP) is evaluated extensively using two different manipulator systems in simulation and in real conditions. Results show that GSTOMP improves task success compared to simple LfD approaches employed by the state-of-the-art collaborative robots. Moreover, it is shown that transferring skills is feasible and with good performance. Finally, the proposed approach is compared against a plethora of state-of-the-art motion planners. The results show that the motion planning performance is comparable or better than the state-of-the-art.

A Novel Local Motion Planning Framework for Autonomous Vehicles Based on Resistance Network and Model Predictive Control

This paper presents a novel local motion planning framework in a hierarchical manner for autonomous vehicles to follow a trajectory and agilely avoid obstacles. In the upper layer, a new path-planning method based on the resistance network is applied to plan behaviors (e.g. lane keeping or changing), where the human-like factors can be included to simulate different driver styles, such as the aggressive, moderate, and conservative. The planned results (i.e. the lane-change command and the local planned path) will guide the lower-layer planner to decide the local motion. In the lower layer, for the sake of simplicity and alleviation of the computational burden, two separate model predictive controllers (MPC) based on a point-mass kinematic model are utilized for both longitudinal and lateral motion planning. Finally, a super-twisting sliding mode controller (STSMC) based motion tracker is designed to show the feasibility of the proposed decoupled planning method and decide the desired control actions of autonomous vehicles. Several scenarios are defined to comprehensively test and demonstrate the effectiveness and the real-time applicability of the new motion-planning framework. The results show that the proposed method performs very well in the planning and tracking process and takes less than $\text{25 ms}$ for the whole planning process, which can be easily implemented in real-world applications.

Active Perception and Control From Temporal Logic Specifications

Next-generation autonomous systems must execute complex tasks in uncertain environments. Active perception, where an autonomous agent selects actions to increase knowledge about the environment, has gained traction in recent years for motion planning under uncertainty. One prominent approach is planning in the belief space. However, most belief-space planning starts with a known reward function, which can be difficult to specify for complex tasks. On the other hand, symbolic control methods automatically synthesize controllers to achieve logical specifications, but often do not deal well with uncertainty. In this letter, we propose a framework for scalable task and motion planning in uncertain environments that combines the best of belief-space planning and symbolic control. Specifically, we provide a counterexample-guided-inductive-synthesis algorithm for probabilistic temporal logic over reals (PRTL) specifications in the belief space. Our method automatically generates actions that improve confidence in a belief when necessary, thus using active perception to satisfy PRTL specifications.

Optimal whole-body motion planning of humanoids in cluttered environments

Due to high degrees-of-freedom of humanoids and induced redundancy, there are multiple ways of performing a given manipulation task. Finding optimal ways of performing tasks is one desirable property of any motion planning framework. This includes optimizing both the path with respect to a certain objective function and also the final pre-grasp or goal position. Additionally, a variety of constraints need to be satisfied such as stability, self-collision and collision with objects in the environment and also kinematic loop-closure formed during the task. In this paper, an asymptotically optimal sampling based approach for generating motion plans is presented. A novel constraint solver extension to the bidirectional Fast Marching Trees (BFMT*) algorithm in the form of a way-point generator is proposed such that it can be applied for whole-body motion planning of humanoids. Moreover, a comparison of the performance of the proposed extension of BFMT* and the state-of-art RRT* based motion planner is shown. A gradient based inverse kinematics solver has also been implemented in combination with an optimization technique to generate goal configurations in order to ensure optimality in the pre-grasp position. The efficacy of the proposed approach is evaluated in a simulation environment on Hubo+ robot model. The results show a significant improvement in path costs, as well as overall optimality of given tasks for the proposed approach. Additionally, rigorous analysis over the choice of planning algorithms considered in this paper is present for the considered scenarios.

Whole-body motion and footstep planning for humanoid robots with multi-heuristic search

In this paper, we present a motion planning framework for humanoid robots that combines whole-body motions as well as footsteps under a quasi-static flat ground plane assumption. Traditionally, these two have been treated as separate research domains. One of the major challenges behind whole-body motion planning is the high DoF (Degrees of Freedom) nature of the problem, in addition to strict constraints on obstacle avoidance and stability. On the other hand footstep planning on its own is a comparatively simpler problem due to the low DoF search space, but coalescing it into a larger framework that includes whole-body motion planning adds further complexity in reaching a solution within a suitable time frame that satisfies all the constraints. In this work, we treat motion planning as a graph search problem, and employ Shared Multi-heuristic A* (SMHA*) to generate efficient, stable and collision-free motion plans given only the starting state of the robot and the desired end-effector pose.

Mission Aware Motion Planning (MAP) Framework With Physical and Geographical Constraints for a Swarm of Mobile Stations.

In this paper, we propose a mission aware motion planning (MAP) framework for a swarm of autonomous unmanned ground vehicles (UGVs) or mobile stations in an uncertain environment for efficient supply of resources/services to unmanned aerial vehicles (UAVs) performing a specific mission. The MAP framework consists of two levels, namely, centralized mission planning and decentralized motion planning. On the first level, the centralized mission planning algorithm estimates the density of UAV in a given environment for determining the number of UGVs and their initial operating location. In the subsequent level, a decentralized motion planning algorithm which provides a closed-form expression for velocity command using adaptive density estimation has been proposed. Further, the physical and geographical constraints are integrated into motion planning. A Monte-Carlo simulation is performed to evaluate the advantages of the MAP over distributed stationary stations (DSSs) often used in the literature. The obtained results clearly indicate that in comparison with DSS, MAP reduces the average distance traveled by UAVs about 20%, reduces the loss of mission time by 90 s per interruption and power loss by 3 dB.

A Motion Planning and Tracking Framework for Autonomous Vehicles Based on Artificial Potential Field Elaborated Resistance Network Approach

This paper presents a novel motion planning and tracking framework for automated vehicles based on artificial potential field (APF) elaborated resistance approach. Motion planning is one of the key parts of autonomous driving, which plans a sequence of movement states to help vehicles drive safely, comfortably, economically, human-like, etc. In this paper, the APF method is used to assign different potential functions to different obstacles and road boundaries; while the drivable area is meshed and assigned resistance values in each edge based on the potential functions. A local current comparison method is employed to find a collision-free path. As opposed to a path, the vehicle motion or trajectory should be planned spatiotemporally. Therefore, the entire planning process is divided into two spaces, namely the virtual and actual. In the virtual space, the vehicle trajectory is predicted and executed step by step over a short horizon with the current vehicle speed. Then, the predicted trajectory is evaluated to decide if the speed should be kept or changed. Finally, it will be sent to the actual space, where an experimentally validated Carsim model controlled by a model predictive controller is used to track the planned trajectory. Several case studies are presented to demonstrate the effectiveness of the proposed framework.

A Smart Many-Core Implementation of a Motion Planning Framework along a Reference Path for Autonomous Cars

Research on autonomous cars, early intensified in the 1990s, is becoming one of the main research paths in automotive industry. Recent works use Rapidly-exploring Random Trees to explore the state space along a given reference path, and to compute the minimum time collision-free path in real time. Those methods do not require good approximations of the reference path, they are able to cope with discontinuous routes, they are capable of navigating in realistic traffic scenarios, and they derive their power from an extensive computational effort directed to improve the quality of the trajectory from step to step. In this paper, we focus on re-engineering an existing state-of-the-art sequential algorithm to obtain a CUDA-based GPGPU (General Purpose Graphics Processing Units) implementation. To do that, we show how to partition the original algorithm among several working threads running on the GPU, how to propagate information among threads, and how to synchronize those threads. We also give detailed evidence on how to organize memory transfers between the CPU and the GPU (and among different CUDA kernels) such that planning times are optimized and the available memory is not exceeded while storing massive amounts of fuse data. To sum up, in our application the GPU is used for all main operations, the entire application is developed in the CUDA language, and specific attention is paid to concurrency, synchronization, and data communication. We run experiments on several real scenarios, comparing the GPU implementation with the CPU one in terms of the quality of the generated paths and in terms of computation (wall-clock) times. The results of our experiments show that embedded GPUs can be used as an enabler for real-time applications of computationally expensive planning approaches.