Autonomous Robots

We present a vision- and ladar-based approach to autonomous driving on rural and desert roads that has been tested extensively in a closed-loop system. The vision component uses Gabor wavelet filters for texture analysis to find ruts and tracks from which the road vanishing point can be inferred via Hough-style voting, yielding a direction estimate for steering control. The ladar component projects detected obstacles along the road direction onto the plane of the front of the vehicle and tracks the 1-D obstacle “gap” presumed due to the road to yield a lateral offset estimate. Several image- and state-based tests to detect failure conditions such as off-road poses (i.e., there is no road to follow) and poor lighting due to sun glare or distracting shadows are also explained. The system’s efficacy is demonstrated with analysis of diverse logged data including from the 2005 DARPA Grand Challenge, as well as tests with full control of a vehicle over 15 km of difficult roads at up to 37 km/h with no waypoints.

MSRox is a wheeled mobile robot with two actuated degrees of freedom which enables it to have smooth motion on flat surfaces. It has the capability of climbing stairs and traversing obstacles, and adaptability toward uphill, downhill and slope surfaces. MSRox with 82 cm in length, 54 cm in width and 29 cm in height has been designed to climb stairs of 10 cm in height and 15 cm in width; nevertheless, it has the capability of climbing stairs up to about 17 cm in height and unlimited widt. In this paper, the motion systems and the capabilities of MSRox are described. Furthermore, experimental results of stair climbing and a comparison of the results with others are presented.

When a battery-powered robot needs to operate for a long period of time, optimizing its energy consumption becomes critical. Driving motors are a major source of power consumption for mobile robots. In this paper, we study the problem of finding optimal paths and velocity profiles for car-like robots so as to minimize the energy consumed during motion. We start with an established model for energy consumption of DC motors. We first study the problem of finding the energy optimal velocity profiles, given a path for the robot. We present closed form solutions for the unconstrained case and for the case where there is a bound on maximum velocity. We then study a general problem of finding an energy optimal path along with a velocity profile, given a starting and goal position and orientation for the robot. Along the path, the instantaneous velocity of the robot may be bounded as a function of its turning radius, which in turn affects the energy consumption. Unlike minimum length paths, minimum energy paths may contain circular segments of varying radii. We show how to efficiently construct a graph which generalizes Dubins’ paths by including segments with arbitrary radii. Our algorithm uses the closed-form solution for the optimal velocity profiles as a subroutine to find the minimum energy trajectories, up to a fine discretization. We investigate the structure of energy-optimal paths and highlight instances where these paths deviate from the minimum length Dubins’ curves. In addition, we present a calibration method to find energy model parameters. Finally, we present results from experiments conducted on a custom-built robot for following optimal velocity profiles.

Robots are said to be capable of self-assembly when they can autonomously form physical connections with each other. By examining different ways in which a system can use self-assembly (i.e., different strategies), we demonstrate and quantify the performance costs and benefits of (i) acting as a physically larger self-assembled entity, (ii) letting the system choose when and if to self-assemble, (iii) coordinating the sensing and actuation of the connected robots so that they respond to the environment as a single collective entity. Our analysis is primarily based on real world experiments in a hill crossing task. The configuration of the hill is not known by the robots in advance—the hill can be present or absent, and can vary in steepness and orientation. In some configurations, the robots can overcome the hill more quickly by navigating individually, while other configurations require the robots to self-assemble to overcome the hill. We demonstrate the applicability of our self-assembly strategies to two other tasks—hole crossing and robot rescue—for which we present further proof-of-concept experiments with real robots.

In this paper, we address the problem of determining whether a mobile robot, called the pursuer, is able to maintain strong mutual visibility (a visibility notion between regions over a convex partition of the environment) of an antagonist agent, called the evader. We frame the problem as a non cooperative game. We consider the case in which the pursuer and the evader move at bounded speed, traveling in a known polygonal environment with or without holes, and in which there are no restrictions as to the distance that might separate the agents. Unlike our previous efforts (Murrieta-Cid et al. in Int J Robot Res 26:233–253, 2007), we give special attention to the combinatorial problem that arises when searching for a solution through visiting several locations in an environment with obstacles. In this paper we take a step further, namely, we assume an antagonistic evader who moves continuously and unpredictably, but with a constraint over its set of admissible motion policies, as the evader moves in the shortest-path roadmap, also called the reduced visibility graph (RVG). The pursuer does not know which among the possible paths over the RVG the evader will choose, but the pursuer is free to move within all the environment. We provide a constructive method to solve the decision problem of determining whether or not the pursuer is able to maintain strong mutual visibility of the evader. This method is based on an algorithm that computes the safe areas (areas that keep evader surveillance) at all times. We prove decidability of this problem, and provide a complexity measure to this evader surveillance game; both contributions hold for any general polygonal environment that might or not contain holes. All our algorithms have been implemented and we show simulation results.

Doors are common objects in indoor environments and their detection can be used in robotic tasks such as map-building, navigation and positioning. This work presents a new approach to door-detection in indoor environments using computer vision. Doors are found in gray-level images by detecting the borders of their architraves. A variation of the Hough Transform is used in order to extract the segments in the image after applying the Canny edge detector. Features like length, direction, or distance between segments are used by a fuzzy system to analyze whether the relationship between them reveals the existence of doors. The system has been designed to detect rectangular doors typical of many indoor environments by the use of expert knowledge. Besides, a tuning mechanism based on a genetic algorithm is proposed to improve the performance of the system according to the particularities of the environment in which it is going to be employed. A large database of images containing doors of our building, seen from different angles and distances, has been created to test the performance of the system before and after the tuning process. The system has shown the ability to detect rectangular doors under heavy perspective deformations and it is fast enough to be used for real-time applications in a mobile robot.

This paper presents a visual homing method for a robot moving on the ground plane. The approach employs a set of omnidirectional images acquired previously at different locations (including the goal position) in the environment, and the current image taken by the robot. We present as contribution a method to obtain the relative angles between all these locations, using the computation of the 1D trifocal tensor between views and an indirect angle estimation procedure. The tensor is particularly well suited for planar motion and provides important robustness properties to our technique. Another contribution of our paper is a new control law that uses the available angles, with no range information involved, to drive the robot to the goal. Therefore, our method takes advantage of the strengths of omnidirectional vision, which provides a wide field of view and very precise angular information. We present a formal proof of the stability of the proposed control law. The performance of our approach is illustrated through simulations and different sets of experiments with real images.