Luis Montano

Nearness diagram navigation (ND): a new real time collision avoidance approach

This paper presents a new real-time collision avoidance approach for mobile robots. The nearness diagram method (ND) performs a high level information extraction and interpretation of the environment. Subsequently, this information is used to generate the motion commands. The proposed approach is well-suited to deal with unknown, unstructured and dynamic environments, where problems of other approaches are avoided. Some experimental results are shown using an holonomic mobile base to demonstrate the usefulness of the method.

Probabilistic scan matching for motion estimation in unstructured environments

This paper presents a probabilistic scan matching algorithm to estimate the robot planar displacement by matching dense two-dimensional range scans. The general framework follows an iterative process of two steps: (i) computation of correspondences between scans, and (ii) estimation of the relative displacement. The contribution is a probabilistic modelling of this process that takes into account all the uncertainties involved: the uncertainty of the displacement of the sensor and the measurement noises. Furthermore, it also considers all the possible correspondences resulting from these uncertainties. This technique has been implemented and tested on a real vehicle. The experiments illustrate how the performances of this method are better than previous geometric ones in terms of robustness, accuracy and convergence.

A "divide and conquer" strategy based on situations to achieve reactive collision avoidance in troublesome scenarios

This paper addresses reactive collision avoidance for robots that move in arduous environments (i.e., very dense, complex and cluttered). To achieve this goal, the technique simplifies the difficulty of the navigation by a divide and conquer strategy, which is based on identifying navigational situations and applying the corresponding motion laws. The state of the art in reactive navigation still presents classic limitations such as trap situations due to U-shape obstacles, difficulty to achieve motion among very close obstacles, to obtain oscillation-free and stable motion, to move over directions far from the goal direction or towards the obstacles, or to tune the heuristic or internal parameters. This paper presents a method that overcomes all these limitations. As a result, navigation with this method is successfully achieved in scenarios where existing techniques present a high degree of difficulty to navigate. Outstanding navigation results are reported using a wheelchair vehicle. A discussion and comparison with existing techniques is provided.

Global nearness diagram navigation (GND)

Presents the global nearness diagram navigation system for mobile robots. The GND generates motion commands to drive a robot safely between locations, whilst avoiding collisions. This system has all the advantages of using the reactive scheme nearness diagram (ND), while having the ability to reason and plan globally (reaching global convergence to the navigation problem). This framework has been extensively tested using a holonomic mobile base equipped with a laser range-finder. Experiments in unknown, unstructured, dynamic and complex environments are reported to validate the system.

Extending Collision Avoidance Methods to Consider the Vehicle Shape, Kinematics, and Dynamics of a Mobile Robot

Most collision avoidance methods do not consider the vehicle shape and its kinematic and dynamic constraints, assuming the robot to be point-like and omnidirectional with no acceleration constraints. The contribution of this paper is a methodology to consider the exact shape and kinematics, as well as the effects of dynamics in the collision avoidance layer, since the original avoidance method does not address them. This is achievable by abstracting the constraints from the avoidance methods in such a way that when the method is applied, the constraints already have been considered. This study is a starting point to extend the domain of applicability to a wide range of collision avoidance methods.

Motion planning in dynamic environments using the velocity space

This paper addresses a method for robot motion planning in dynamic environments, avoiding the moving and static obstacles while the robot drives towards the goal. The method maps the dynamic environment into a velocity space, using the concept of estimated arriving time to compute the times to potential collision and potential escape. The problem of finding the best motion command is directly treated in the velocity space, providing the trajectory which satisfies an optimization criterium (typically the minimum time or the shortest path). In this work the method is applied to robots which are subject to both kinematic constraints (i.e. involving the configuration parameters of the robot and their derivatives), and dynamic constraints, (i.e. the constraints imposed by the dynamics of the robot and the limits of its actuators). Some experimental results are discussed.

Modeling the Static and the Dynamic Parts of the Environment to Improve Sensor-based Navigation

This paper addresses the modeling of the static and dynamic parts of the scenario and how to use this information within a real sensor-based navigation system. The contribution in the modeling aspect is a formulation of the Detection and Tracking of Mobile Objects and the Simultaneous Localization and Map Building in such a way that the nature (static/dynamic) of the observations is included in the estimation process. This is achieved by a set of filters tracking the moving objects and a map of the static structure constructed on line. In addition, this paper discusses how this modeling module is integrated in a real sensor-based navigation system taking advantage selectively of the dynamic and static information. The experimental results confirm that the complete navigation system is able to move a vehicle in unknown and dynamic scenarios. Furthermore, the system overcomes many of the limitations of previous systems associated to the ability to distinguish the nature of the parts of the scenario.

Real-time robot navigation in unstructured environments using a 3D laser rangefinder

In this paper a technique for real-time robot navigation is presented. Off-line planned trajectories and motions are modified in real-time to avoid obstacles, using a reactive behaviour. The information about the environment is provided to the control system of the robot by a rotating 3D laser sensor which have two degrees of freedom. Using this sensor, three dimensional information can be obtained, and can be used simultaneously for obstacle avoidance, robot self-localization and for 3D local map building. In this work we focus the attention in the obstacle avoidance problem with this kind of sensor. Experimental results in different indoor environments are reported, which show the versatility of the proposed technique to carry out typical tasks such as wall following, door crossing and motion in a room with several obstacles.

Abstracting Vehicle Shape and Kinematic Constraints from Obstacle Avoidance Methods

Most obstacle avoidance techniques do not take into account vehicle shape and kinematic constraints. They assume a punctual and omnidirectional vehicle and thus they are doomed to rely on approximations when used on real vehicles. Our main contribution is a framework to consider shape and kinematics together in an exact manner in the obstacle avoidance process, by abstracting these constraints from the avoidance method usage. Our approach can be applied to many non-holonomic vehicles with arbitrary shape.For these vehicles, the configuration space is three-dimensional, while the control space is two-dimensional. The main idea is to construct (centred on the robot at any time) the two-dimensional manifold of the configuration space that is defined by elementary circular paths. This manifold contains all the configurations that can be attained at each step of the obstacle avoidance and is thus general for all methods. Another important contribution of the paper is the exact calculus of the obstacle representation in this manifold for any robot shape (i.e. the configuration regions in collision). Finally, we propose a change of coordinates of this manifold so that the elementary paths become straight lines. Therefore, the three-dimensional obstacle avoidance problem with kinematic constraints is transformed into the simple obstacle avoidance problem for a point moving in a two-dimensional space without any kinematic restriction (the usual approximation in obstacle avoidance). Thus, existing avoidance techniques become applicable.The relevance of this proposal is to improve the domain of applicability of a wide range of obstacle avoidance methods. We validated the technique by integrating two avoidance methods in our framework and performing tests in the real robot.

A kinematic and dynamic model-based motion controller for mobile robots

The paper presents a model for motion generation of differential-drive mobile robots. The parameters of the dynamic model allow adjusting the robot translational and rotational behaviours separately. The model takes into account the robot kinematic and dynamic constraints, making the velocities and accelerations bounded and compatible with those the robot can perform. The main contribution of the paper is to use the model itself as a motion controller: under soft hypothesis on the velocities and accelerations, this approach allows an easy tuning of the controller parameters. A system stability and parameters sensitivity analysis is developed, in order to get guidelines for controller tuning. The clear physical sense of the parameters make this tuning easy and intuitive. Experimental results involving a real mobile robot show the performance of this approach.