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.

Sampling-Based Robot Motion Planning: A Review

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 vehicle's 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.

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

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.

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

During the last decade, sampling-based algorithms for path planning have gained considerable attention. The RRT*, a variant of RRT (rapidly-exploring random trees), is of particular concern to researchers due to its asymptotic optimality. However, the limits of the slow convergence rate of RRT* makes it inefficient for applications. For the purposes of overcoming these limitations, this paper proposes a novel algorithm, PQ-RRT*, which combines the strengths of P-RRT* (potential functions based RRT*) and Quick-RRT*. PQ-RRT* guarantees a fast convergence to an optimal solution and generates a better initial solution. The asymptotic optimality and fast convergence of the proposed algorithm are proved in this paper. Comparisons of PQ-RRT* with P-RRT* and Quick-RRT* in four benchmarks verify the effectiveness of the proposed algorithm.

PQ-RRT*: An improved path planning algorithm for mobile robots

The problem of generating a smooth reference path, given a finite family of discrete, locally optimal paths, is investigated. A finite discretization of the environment results in a sequence of obstacle-free square cells. The generated path must lie inside the channel generated by these obstacle-free cells, while minimizing certain performance criteria. Two constrained optimization problems are formulated and solved subject to the given geometric (linear) constraints and boundary conditions in order to generate a library of B-spline path templates offline. These templates are recalled during implementation and are merged together on the fly in order to construct a smooth and feasible reference path to be followed by a closed-loop tracking controller. Combined with a discrete path planner, the proposed algorithm provides a complete solution to the obstacle-free path-generation problem for an unmanned aerial vehicle in a computationally efficient manner, which is suitable for real-time implementation.

/pdf/on-line-path-generation-for-unmanned-aerial-vehicles-using-b-4tqwjzrgw6.pdf

On-Line Path Generation for Unmanned Aerial Vehicles Using B-Spline Path Templates

In this paper we introduce a roadmap algorithm for generating collision-free paths in terms of cubic B-spline curves for unmanned vehicles used in mining operations. The algorithm automatically generates collision-free paths that are curvature continuous with an upper bounded curvature and a small slope discontinuity of curvature at knots, when we are given the locations of the obstacles, the boundary geometry of the working area, positions and directions of the vehicle at the start, loading, and the goal points. Our algorithm also allows us to find a switch back point where the vehicle reverses its direction to enter the loading area. Examples are provided to demonstrate the effectiveness of the proposed algorithms.

Tetsuya Noda

Papers

Curvature continuous path generation for autonomous vehicle using B-spline curves