Geometric and Bayesian models for safe navigation in dynamic environments

Autonomous navigation in open and dynamic environments is an important challenge, requiring to solve several difficult research problems located on the cutting edge of the state of the art. Basically, these problems may be classified into three main categories: (a) SLAM in dynamic environments; (b) detection, characterization, and behavior prediction of the potential moving obstacles; and (c) online motion planning and safe navigation decision based on world state predictions. This paper addresses some aspects of these problems and presents our latest approaches and results. The solutions we have implemented are mainly based on the followings paradigms: multiscale world representation of static obstacles based on the wavelet occupancy grid; adaptative clustering for moving obstacle detection inspired on Kohonen networks and the growing neural gas algorithm; and characterization and motion prediction of the observed moving entities using Hidden Markov Models coupled with a novel algorithm for structure and parameter learnin

Trajectory Planning Amidst Moving Obstacles: Path-Velocity Decomposition Revisited

This paper addresses trajectory planning for a robot subject to dynamic constraints and moving in a dynamic workspace. A car-like robot A with bounded velocity and acceleration, moving in a dynamic two-dimensional workspace is considered. The solution proposed is an extension of the path-velocity decomposition which is a practical way to address trajectory planning in dynamic workspaces. However it presents a serious drawback: it cannot find a solution if a moving obstacle stops right on the computed path. Previous answers to this problem were to consider sets of candidate paths. The answer proposed in this paper makes use of the novel concept of adjacent paths (like adjacent lanes of the roadway). A set of adjacent paths, one of which leads A to its goal, is computed. Then, assuming that A is able to shift from one path to an adjacent one freely, the motion of A along and between these paths is determined so as to avoid the moving obstacles. The fact that it is possible to switch sev..

Trajectory Planning in a Dynamic Workspace: a `State-Time Space' Approach

This paper addresses trajectory planning in a dynamic workspace, i.e. motion planning for a robot subject to dynamic constraints and moving in a workspace with moving obstacles. First is introduced the novel concept of statetime space, i.e. the state space of the robot augmented of the time dimension. Like configuration space which is a tool to formulate path planning problems, state-time space is a tool to formulate trajectory planning in dynamic workspace problems. It permits to study the different aspects of dynamic trajectory planning, i.e. moving obstacles and dynamic constraints, in a unified way. Then this new concept is applied to the case of a car-like robot subject to dynamic constraints and moving along a given path on a dynamic planar workspace. A near-time-optimal approach that searches the solution trajectory over a restricted set of canonical trajectories is presented. These canonical trajectories are defined as having discrete and piecewise constant acceler..

Dynamic Trajectory Planning with Dynamic Constraints: a `State-Time Space' Approach

This paper addresses dynamic trajectory planning which is defined as trajectory planning for a robot subject to dynamic constraints and moving in a dynamic workspace, i.e. with moving obstacles. To begin with, we propose the novel concept of state-time space as a tool to formulate dynamic trajectory planning problems. The state-time space of a robot is its state space augmented of the time dimension. It permits to study the different aspects of dynamic trajectory planning in a unified way. Thus the constraints imposed by both the moving obstacles and the dynamic constraints can be represented by static forbidden regions of state-time space. Besides a trajectory maps to a curve in state-time space hence dynamic trajectory planning simply consists in finding a curve in state-time space. Then we apply this new concept in order to determine a time-optimal trajectory for a car-like robot subject to dynamic constraints and moving along a given path on a dynamic planar workspace. We present a..

Trajectory Planning in Dynamic Workspaces: a `State-Time Space' Approach

This paper addresses trajectory planning in dynamic workspaces, i.e. trajectory planning for a robot subject to dynamic constraints and moving in a workspace with moving obstacles. First is introduced the novel concept of state-time space, i.e. the state space of the robot augmented of the time dimension. Like the concept of configuration space which is a tool to formulate path planning problems, state-time space is a tool to formulate trajectory planning in dynamic workspaces problems. It permits to study the different aspects of dynamic trajectory planning, i.e. moving obstacles and dynamic constraints, in a unified way. Then this new concept is applied to the case of a car-like robot subject to dynamic constraints and moving along a given path on a dynamic planar workspace. A near-time-optimal approach that searches the solution trajectory over a restricted set of canonical trajectories is presented. These canonical trajectories are defined as having discrete and piecewise constan..

Kinodynamic Planning in a Structured and Time-Varying Workspace

This paper deals with a kinodynamic trajectory planning problem that we call the `highway problem'. It consists in planning a time-optimal trajectory for a robot which is travelling in a structured workspace amidst moving obstacles and which is subject to constraints on its velocity and acceleration. By structured workspace, we mean that there are lanes within which the robot is able to move. A lane is characterized by its `spine', i.e. a one-dimensional curve. The robot has to follow a predetermined lane but it may also shift from its lane to an adjacent one. This paper presents an efficient method which determines an approximate time-optimal solution to the highway problem. The approach consists in discretizing time and selecting the acceleration applied to the robot among a discrete set. These hypotheses make it possible to define a grid in the robot's state-time space, i.e. the robot's state (or phase) space augmented of the time dimension. This grid is then searched in order to fi..

Continuous-Curvature Path Planning for Car-Like Vehicles

In this paper, we consider path planning for a car-like vehicle. We defin

Integrating Uncertainty And Landmarks In Path Planning For Car-Like Robots

This paper presents the first path planner taking into account both non-holonomic and uncertainty constraints. The case of a car-like robot equipped with a relative localization system and therefore subject to uncertainty on its configuration is considered. Assuming the existence of landmarks allowing the robot to relocalize itself in particular places, we present an algorithm that computes paths that are both feasible and robust. In other words, they respect the non-holonomic constraints of a car-like robot and the robot is assured to reach its goal by following them no matter what its control and sensing errors are