Motion planning is one of the most important areas of robotics research. The complexity of the motion-planning problem has hindered the development of practical algorithms. This paper surveys the work on gross-motion planning, including motion planners for point robots, rigid robots, and manipulators in stationary, time-varying, constrained, and movable-object environments. The general issues in motion planning are explained. Recent approaches and their performances are briefly described, and possible future research directions are discussed.

Gross motion planning—a survey

A distributed architecture articulated around the CODGER (communication database with geometric reasoning) knowledge database is described for a mobile robot system that includes both perception and navigation tools. Results are described for vision and navigation tests using a mobile testbed that integrates perception and navigation capabilities that are based on two types of vision algorithms: color vision for road following, and 3-D vision for obstacle detection and avoidance. The perception modules are integrated into a system that allows the vehicle to drive continuously in an actual outdoor environment. The resulting system is able to navigate continuously on roads while avoiding obstacles. >

Vision and navigation for the Carnegie-Mellon Navlab

An algorithm is presented for wheeled mobile robot trajectory generation that achieves a high degree of generality and efficiency. The generality derives from numerical linearization and inversion of forward models of propulsion, suspension, and motion for any type of vehicle. Efficiency is achieved by using fast numerical optimization techniques and effective initial guesses for the vehicle controls parameters. This approach can accommodate such effects as rough terrain, vehicle dynamics, models of wheel-terrain interaction, and other effects of interest. It can accommodate boundary and internal constraints while optimizing an objective function that might, for example, involve such criteria as obstacle avoidance, cost, risk, time, or energy consumption in any combination. The algorithm is efficient enough to use in real time due to its use of nonlinear programming techniques that involve searching the space of parameterized vehicle controls. Applications of the presented methods are demonstrated for planetary rovers.

Optimal Rough Terrain Trajectory Generation for Wheeled Mobile Robots

A method for planning the motions of autonomous vehicles moving on general terrains is presented that obtains the geometric path and vehicle speeds that minimize motion time considering vehicle dynamics, terrain topography, obstacles, and surface mobility. The terrain is represented by a smooth cubic B patch, and the geometric path consists of a B spline curve mapped to the surface. The time-optimal motions are computed by first obtaining the best obstacle-free path from all paths represented by a uniform grid. This path is further optimized using a local optimization procedure, using the optimal motion time along the path as the cost function and the control points of a B spline as the optimizing parameters. Examples are presented that demonstrate the method for a simple dynamic model of a vehicle moving on mountainous terrain. >

Dynamic motion planning of autonomous vehicles

Animal behavior as a paradigm for developing robot autonomy

In a 2-1/D world an isolines-based world representation is employed. An algorithm of navigation is proposed based upon polygonization of the isolines, and use of the vertices of the polygon as nodes in the graph search. Quanitative recommendations are given concerning the required density of isolines and the error of polygonization. When a physical model of mechanical motion is applied, this algorithm of navigation provides minimum-time trajectories of motion. The results of navigation are illustrated using a simulation system developed for an Intelligent Mobile Autonomous System (unmanned robot).

Minimum-time navigation of an unmanned mobile robot in a 2-1/2D world with obstacles

A real-time system for the control of an autonomous vehicle consisting of a nested hierarchy of control modules is described. The focus is on the PILOT module which provdies the real-time guidance of the system. It is responsible for the generation and tracking of dynamically feasible trajectories which follow the generally planned path and avoid local obstacles. The PILOT operation is based on a combination of simple rules and heuristic search. The state space consists of two parts: the lower level state space which represents the model of the world as a set of relationships among the units of vehicle state variables representing steering, acceleration, and velocity, as well as positions of obstacles, and the upper level state space where the relationships among generalized objects are represented as rules. Rules provide simple elementary maneuvers, and are applied when the upper level situation is easily recognized. Otherwise the situations are handled by the lower level search. Example runs of the vehicle are simulated to demonstrate the complexity of behavior. The algorithm has also been tested on a gas powered dune buggy developed at Drexel University.

A real-time guidance system for an autonomous vehicle

This paper describes recent research results in the area of autonomous robotics. A multiresolutional (pyramidal) system of world representation is applied to the domain required for Intelligent Mobile Autonomous Systems for outdoor application. Problems of generalization, accuracy, and adequacy of representation are formulated for a system with vision and ultrasonic sensors. A method of nested hierarchical planning is applied for this system of representation. A Pilot with behavioral duality is proven to be efficient for a mobile robot. An execution controller is explored based upon a production system providing near-minimum time operation.

Multiresolutional Pyramidal Knowledge Representation And Algorithmic Basis Of IMAS-2

This paper describes a system of guidance for an intelligent mobile autonomous system (autonomous robot) based upon an algorithm of "pilot decision making" which incorporates different strategies of operation. Depending on the set of circumstances including the level of "informedness", initial data, concrete environment, and so on, the "personality" of PILOT is being selected between two alternatives: 1) a diligent strategist which tends to explore all available trajectories off-line and be prepared to follow one of them precisely, and 2) a hasty decision maker inclined to make a choice of solution in a rather reckless manner base upon short term alternatives not regarding long term consequences. Simulation shows that these two personalities support each other in a beneficial way.

D. Gaw

Papers

Minimum-time navigation of an unmanned mobile robot in a 2-1/2D world with obstacles

A real-time guidance system for an autonomous vehicle

Multiresolutional Pyramidal Knowledge Representation And Algorithmic Basis Of IMAS-2

Simulation Of Dual Behavior Of An Autonomous System