Mohamed Elbanhawi

Sampling-Based Robot Motion Planning: A Review

Motion planning is a fundamental research area in robotics. Sampling-based methods offer an efficient solution for what is otherwise a rather challenging dilemma of path planning. Consequently, these methods have been extended further away from basic robot planning into further difficult scenarios and diverse applications. A comprehensive survey of the growing body of work in sampling-based planning is given here. Simulations are executed to evaluate some of the proposed planners and highlight some of the implementation details that are often left unspecified. An emphasis is placed on contemporary research directions in this field. We address planners that tackle current issues in robotics. For instance, real-life kinodynamic planning, optimal planning, replanning in dynamic environments, and planning under uncertainty are discussed. The aim of this paper is to survey the state of the art in motion planning and to assess selected planners, examine implementation details and above all shed a light on the current challenges in motion planning and the promising approaches that will potentially overcome those problems.

Continuous Path Smoothing for Car-Like Robots Using B-Spline Curves

A practical approach for generating motion paths with continuous steering for car-like mobile robots is presented here. This paper addresses two key issues in robot motion planning; path continuity and maximum curvature constraint for nonholonomic robots. The advantage of this new method is that it allows robots to account for their constraints in an efficient manner that facilitates real-time planning. B-spline curves are leveraged for their robustness and practical synthesis to model the vehicle’s path. Comparative navigational-based analyses are presented to selected appropriate curve and nominate its parameters. Path continuity is achieved by utilizing a single path, to represent the trajectory, with no limitations on path, or orientation. The path parameters are formulated with respect to the robot’s constraints. Maximum curvature is satisfied locally, in every segment using a smoothing algorithm, if needed. It is demonstrated that any local modifications of single sections have minimal effect on the entire path. Rigorous simulations are presented, to highlight the benefits of the proposed method, in comparison to existing approaches with regards to continuity, curvature control, path length and resulting acceleration. Experimental results validate that our approach mimics human steering with high accuracy. Accordingly, efficiently formulated continuous paths ultimately contribute towards passenger comfort improvement. Using presented approach, autonomous vehicles generate and follow paths that humans are accustomed to, with minimum disturbances.

In the Passenger Seat: Investigating Ride Comfort Measures in Autonomous Cars

The prospect of driverless cars wide-scale deployment is imminent owing to the advances in robotics, computational power, communications, and sensor technologies. This promises highway fatality reductions and improvements in traffic and fuel efficiency. Our understanding of the effects arising from commuting in autonomous cars is still limited. The novel concept of the loss of driver controllability is introduced here. It requires a reassessment of vehicles comfort criteria. In this review paper, traditional comfort measures are examined and autonomous passenger awareness factors are proposed. We categorize path-planning methods in light of the offered factors. The objective of the review presented in this article is to highlight the gap in path planning from a passenger comfort perspective and propose some research solutions. It is expected that this investigation will generate more research interest and bring innovative solutions into this field.

Randomized Bidirectional B-Spline Parameterization Motion Planning

This paper presents a motion planner tailored for particular requirements for robotic car navigation. We leverage B-spline curve properties to include vehicles constraint requirements, thus lowering the search dimensionality. An algorithm, which combines competent exploratory nature of the randomized search methods with vector-valued parameterization steering, is developed here. Vehicles limitations, along with obstacles constraints, are satisfied without being hindered by numerical integration and control space discretization of traditional randomized kinodynamic planners. We rely on newly developed theoretical underpinnings to overcome performance issues in rapidly exploring random tree (RRT) solutions. Rigorous simulations and analysis demonstrate that this new approach outperforms recently proposed planners by using an efficient bidirectional RRT-based search, by maintaining continuous state and control spaces, and generating C2 continuous paths, which are realistic inputs suited for mobile robotic applications and passenger vehicles.

Improved manoeuvring of autonomous passenger vehicles: Simulations and field results

This paper describes a novel manoeuvre planning method to attenuate disturbances acting on occupants of autonomous cars as a result of driving behaviour. New research findings suggested that the passengers in autonomous cars might be more prone to motion sickness and thus overall discomfort. The proposed approach is based on a recently developed novel continuous B-spline path smoothing algorithm for car-like steered robots. Two algorithms are designed for urban driving scenarios and steering between two predefined poses. The resulting paths avoid abrupt changes in steering and longitudinal velocity, by maintaining curvature and its high order continuity. We show that this lead to reduced high frequency disturbances in steering and resulting load disturbances on passengers. The presented novel B-spline manoeuvres outperform other planning methods by reducing lateral acceleration and yaw disturbances. New approach was verified by rigorous simulations, numerical and field experimentation. Tests were repeated for a number of different paths and velocities. The reported results are the first spline based parameterisation methods practically applied for autonomous cars planning and re-planning, then validated using both noisy actuation simulations and field experiments.

Examining the Use of B-Splines in Parking Assist Systems

The main objective of the presented study and simulations conducted was to investigate the prospect of using B-spline curves for the automatic parking, i.e. self-driving, or intelligent vehicles. We consider the problem of parallel parking for a non-holonomic vehicle with a known maximum path curvature. The relationship between the properties of the path and the geometry of corresponding parking spot is revealed. The unique properties of B-splines are exploited to synthesize a path that is smooth and of continuous curvature. The contributions of this project are in the generations of better, smooth continuous paths. This improves passenger comfort during the parallel parking maneuver and allow vehicles to park in tighter spots by increasing the feasible range of the parking manoeuver.

Receding horizon lateral vehicle control for pure pursuit path tracking

The assimilation of path planning and motion control is a crucial capability for autonomous vehicles. Pure pursuit controllers are a prevalent class of path tracking algorithms for front wheel steering cars. Nonetheless, their performance is rather limited to relatively low speeds. In this paper, we propose a model predictive active yaw control implementation of pure pursuit path tracking that accommodates the vehicle’s steady state lateral dynamics to improve tracking performance at high speeds. A comparative numerical analysis was under taken between the proposed strategy and the traditional pure pursuit controller scheme. Tests were conducted for three different paths at iteratively increasing speeds from 1 m/s up to 20 m/s. The traditional pure pursuit controller was incapable of maintaining the vehicle stable at speeds upwards of 5m/s. The results show that implementing receding horizon strategy for pure pursuit tracking improves their performance. The contribution is apparent by preserving a relatively constant controller effort and consequently maintaining vehicle stability for speeds up to 100Km/h in different scenarios. A Matlab implementation of the proposed controller and datasets of the experimental paths are provided to supplement this work.

Autonomous Robots Path Planning: An Adaptive Roadmap Approach

Developing algorithms that allow robots to independently navigate unknown environments is a widely researched area of robotics. The potential for autonomous mobile robots use, in industrial and military applications, is boundless. Path planning entails computing a collision free path from a robots current position to a desired target. The problem of path planning for these robots remains underdeveloped. Computational complexity, path optimization and robustness are some of the issues that arise. Current algorithms do not generate general solutions for different situations and require user experience and optimization. Classical algorithms are computationally extensive. This reduces the possibility of their use in real time applications. Additionally, classical algorithms do not allow for any control over attributes of the generated path. A new roadmap path planning algorithm is proposed in this paper. This method generates waypoints, through which the robot can avoid obstacles and reach its goal. At the heart of this algorithm is a method to control the distance of the waypoints from obstacles, without increasing its computational complexity. Several simulations were run to illustrate the robustness and adaptability of this approach, compared to the most commonly used path planning methods.

The Effect of Receding Horizon Pure Pursuit Control on Passenger Comfort in Autonomous Vehicles

Passengers in autonomous vehicles are prone to motion sickness. Receding horizon control of pure pursuit tracking algorithms has been shown to improve path tracking performance. In this paper we present a numerical study on the effect of the receding horizon pure pursuit controller on passenger comfort. Three standard cases at the different speeds are utilized to compare the effect of traditional and receding horizon pure pursuit control on passenger comfort. The results show improvements in passenger comfort at higher speeds using receding horizon control and that path continuity is more influential that optimal tracking control.

Developing a Navigation System for Mobile Robots

Design solution of a novel mobile robot navigation system, presented here, is used to control robot’s locomotion across slippery surfaces. Usually, motion control strategies, are based on assumption of sufficient traction between tyres and the road. Motion across slippery surfaces can endanger the robot and its surroundings. Our solution combines Light Detection and Ranging (LIDAR) measurements with odometry data. It performs well on any surface, regardless of sensing, localization and navigation errors, within an indoor environment, in real-time. An accelerated feature detection method is used to improve LIDAR localization update rate and improve localization accuracy. Experiments conducted validate proposed approach.