Currently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion, lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature. The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existing methods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performance of planning algorithms at all three levels. Some promising areas of future focus have been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical review on planning techniques presented in this paper, along with the associated discussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research.

Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions

To examine the effects of variation in body form on the mechanics of terrestrial locomotion, we used a miniature force platform to measure the ground reaction forces of the smallest and, relative to its mass, one of the fastest invertebrates ever studied, the American cockroach Periplaneta americana (mass = 0.83 g). From 0.44-1.0 ms-1, P. americana used an alternating tripod stepping pattern. Fluctuations in gravitational potential energy and horizontal kinetic energy of the center of mass were nearly in phase, characteristic of a running or bouncing gait. Aerial phases were observed as vertical ground reaction force approached zero at speeds above 1 ms-1. At the highest speeds (1.0-1.5 ms-1 or 50 body lengths per second), P. americana switched to quadrupedal and bipedal running. Stride frequency approached the wing beat frequencies used during flight (27 Hz). High speeds were attained by increasing stride length, whereas stride frequency showed little increase with speed. The mechanical power used to accelerate the center of mass increased curvilinearly with speed. The mass-specific mechanical energy used to move the center of mass a given distance was similar to that measured for animals five orders of magnitude larger in mass, but was only one-hundredth of the metabolic cost.

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Mechanics of a rapid running insect: two-, four- and six-legged locomotion.

We present a motion planner for autonomous highway driving that adapts the state lattice framework pioneered for planetary rover navigation to the structured environment of public roadways. The main contribution of this paper is a search space representation that allows the search algorithm to systematically and efficiently explore both spatial and temporal dimensions in real time. This allows the low-level trajectory planner to assume greater responsibility in planning to follow a leading vehicle, perform lane changes, and merge between other vehicles. We show that our algorithm can readily be accelerated on a GPU, and demonstrate it on an autonomous passenger vehicle.

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Motion planning for autonomous driving with a conformal spatiotemporal lattice

The number of registration solutions in the literature has bloomed recently. The iterative closest point, for example, could be considered as the backbone of many laser-based localization and mapping systems. Although they are widely used, it is a common challenge to compare registration solutions on a fair base. The main limitation is to overcome the lack of accurate ground truth in current data sets, which usually cover environments only over a small range of organization levels. In computer vision, the Stanford 3D Scanning Repository pushed forward point cloud registration algorithms and object modeling fields by providing high-quality scanned objects with precise localization. We aim to provide similar high-caliber working material to the robotic and computer vision communities but with sceneries instead of objects. We propose eight point cloud sequences acquired in locations covering the environment diversity that modern robots are susceptible to encounter, ranging from inside an apartment to a woodland area. The core of the data sets consists of 3D laser point clouds for which supporting data (Gravity, Magnetic North and GPS) are given for each pose. A special effort has been made to ensure global positioning of the scanner within mm-range precision, independent of environmental conditions. This will allow for the development of improved registration algorithms when mapping challenging environments, such as those found in real-world situations.1

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Challenging data sets for point cloud registration algorithms

In this paper we study the quasi-static motion of large legged robots that have many degrees of freedom. While gaited walking may suffice on easy ground, rough and steep terrain requires unique sequences of footsteps and postural adjustments specifically adapted to the terrain's local geometric and physical properties. In this paper we present a planner that computes these motions by combining graph searching to generate a sequence of candidate footfalls with probabilistic sample-based planning to generate continuous motions that reach these footfalls. To improve motion quality, the probabilistic planner derives its sampling strategy from a small set of motion primitives that have been generated offline. The viability of this approach is demonstrated in simulation for the six-legged Lunar vehicle ATHLETE and the humanoid HRP-2 on several example terrains, including one that requires both hand and foot contacts and another that requires rappelling.

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Motion Planning for Legged Robots on Varied Terrain

Exploring Mount Erebus by walking robot

Outdoor mobile robot motion planning and navigation is a challenging problem in artificial intelligence. The search space density and dimensionality, system dynamics and environmental interaction complexity, and the perceptual horizon limitation all contribute to the difficultly of this problem. It is hard to generate a motion plan between arbitrary boundary states that considers sophisticated vehicle dynamics and all feasible actions for nontrivial mobile robot systems. Accomplishing these goals in real time is even more challenging because of dynamic environments and updating perception information.
This thesis develops effective search spaces for mobile robot trajectory generation, motion planning, and navigation in complex environments. Complex environments are defined as worlds where locally optimal motion plans are numerous and where the sensitivity of the cost function is highly dependent on state and motion model fidelity. Examples include domains where obstacles are prevalent, terrain shape is varied, and the consideration of terramechanical effects is important.
Three specific contributions are accomplished. First, a model-predictive trajectory generation technique is developed that numerically linearizes and inverts general predictive motion models to determine parameterized actions that satisfy the two-point boundary value problem. Applications on a number of mobile robot platforms (including skid-steered field robots, planetary rovers with actively articulating chassis, mobile manipulators, and autonomous automobiles) demonstrate the versatility and generality of the presented approach. Second, an adaptive search space is presented that exploits environmental information to maintain feasibility and locally optimize the mapping between nodes and states. Sequential search in the relaxed motion planning graph is shown to produce better (shorter/faster/lower-risk) trajectories in dense obstacle fields without modifying the graph topology. Results demonstrate that a coarse, adaptive search space can produce better solutions faster than dense, fixed search spaces in sufficiently complex environments. Lastly, a receding-horizon model-predictive control method that exploits structure from sequential search to determine trajectory following actions is presented. The action space is parameterized by the regional motion plan and subsequently relaxed through unconstrained optimization. Examples are shown to effectively navigate intricate paths in a natural environment while maintaining a constant horizon.

Adaptive model-predictive motion planning for navigation in complex environments

This paper presents a real-time, kinodynamic planning and information-theoretic exploration framework that enables high-resolution mapping of three-dimensional environments featuring complex concavities and disjoint objects. The proposed approach targets planetary exploration applications and seeks to achieve real-time operation on computationally constrained systems while ensuring energy-efficient information acquisition. Trajectories are selected by maximizing a measure of information gain per an expected execution cost (e.g., time or energy). The proposed trajectory generation formulation is based on state-lattice motion primitives and evaluation of the Cauchy-Schwarz quadratic mutual information (CSQMI) at each lattice state. An expanded search structure is proposed that extends the state-lattice to a finite horizon to enable expansive space coverage while remaining real-time viable. Additionally, compression techniques are employed to reduce the computational burden associated with the CSQMI calculation over expansive environments while preserving fidelity. The performance of the proposed methodology is evaluated through simulated exploration of a three-dimensional terrestrial pit environment by a quadrotor aerial robot which acts as a surrogate for a propulsive vehicle when operating on an airless body.

Computationally efficient information-theoretic exploration of pits and caves

This paper compares a broad cross-section of range sensing technologies for underground void modeling. In this family of applications, a tunnel environment is incrementally mapped with range sensors from a mobile robot to recover scene geometry. Distinguishing contributions of this work include an unprecedented number of configurations evaluated utilizing common methodology and metrics as well as a significant in situ environmental component lacking in prior characterization work. Sensors are experimentally compared against both an ideal geometric target and in example void environments such as a mine and underground tunnel. Three natural groupings of sensors were identified from these results and performances were found to be strongly cost-correlated. While the results presented are specific to the experimental configurations tested, the generality of tunnel environments and the metrics of reconstruction are extensible to a spectrum of outdoor and surface applications.

Comparative evaluation of range sensing technologies for underground void modeling

The goal of the planetary rover project is to prototype an autonomous mobile robot for planetary exploration. The authors have constructed a six-legged walking robot, called the Ambler, that features orthogonal legs, an overlapping gait, and a scanning laser rangefinder to model terrain. To enable the Ambler to walk over rugged terrain, they have combined perception, planning, and real-time control into a comprehensive robotic system. >

Red Whittaker

Papers

Exploring Mount Erebus by walking robot

Adaptive model-predictive motion planning for navigation in complex environments

Computationally efficient information-theoretic exploration of pits and caves

Comparative evaluation of range sensing technologies for underground void modeling

Ambler: a six-legged planetary rover