Fusion of telemetric and visual data from traffic scenes helps exploit synergies between different on-board sensors, which monitor the environment around the ego-vehicle. This paper outlines our approach to sensor data fusion, detection and tracking of objects in a dynamic environment. The approach uses a Bayesian Occupancy Filter to obtain a spatio-temporal grid representation of the traffic scene. We have implemented the approach on our experimental platform on a Lexus car. The data is obtained in traffic scenes typical of urban driving, with multiple road participants. The data fusion results in a model of the dynamic environment of the ego-vehicle. The model serves for the subsequent analysis and interpretation of the traffic scene to enable collision risk estimation for improving the safety of driving.

The ArosDyn project aims to develop embedded software for robust analysis of dynamic scenes in urban traffic environments, in order to estimate and predict collision risks during car driving. The on-board telemetric sensors (lidars) and visual sensors (stereo camera) are used to monitor the environment around the car. The algorithms make use of Bayesian fusion of heterogenous sensor data. The key objective is to process sensor data for robust detection and tracking of multiple moving objects for estimating and predicting collision risks in real time, in order to help avoid potentially dangerous situations.

This paper describes our research work towards the development of an optical guidance system for multiple mobile robots in an indoor environment. The guidance system operates with an environment model, communicates with mobile robots and indicates their target positions by means of a light projection from a laser pointer onto the ground. Processing the image data from a CCD color camera mounted on the mobile robot allows it to detect the laser light beacon on the ground and estimate its relative coordinates. The robot's control system ensures the accurate motion of the robot to the indicated target position. The guidance system subsequently indicates target positions corresponding to a desired route for a specified mobile robot in the fleet. The concept of the optical guidance system, its implementation and experimental results obtained are discussed.

This paper deals with the motion generation approach developed for our omnidirectional mobile robots. Our objective, overall control architecture of the mobile robot, and its motion controller are considered. The key idea and advantages of the proposed motion generation approach are discussed. The approach is implemented and tested on the omnidirectional mobile robot and the experimental results obtained are presented. The operation of the control system is illustrated by a video on the remote control of the robot and the visually-coupled motion of the two robots.

The stabilization of the motion of a nonholonomic vehicle is considered. The control system developed has a two-level architecture. The lower control level operates within the kinematic model of the vehicle to stabilize its motion to a desired trajectory. The upper control level uses the dynamic model of the vehicle and stabilizes the feedback obtained on the lower control level. The operation of the control system is studied when unknown bounded disturbances affect the motion. The adaptive motion control is proposed to deal with uncertain dynamic parameters of the vehicle.

Presents a control architecture for a car-like vehicle moving in a dynamic and partially known environment. The key idea is to plan and carry out sensor-based manoeuvres. The paper focuses on the reactive part of the architecture that features control experts, i.e. parameterized control programs adapted to a specific manoeuvre and capable to react in real-time to unforeseen events. Experimental results obtained with an automatic car are presented for two types of manoeuvres: lane following/changing and parallel parking.

This paper describes the control approach developed to perform parallel parking and returning to traffic manoeuvres for a car capable of autonomous motion. The key idea is to carry out a motion control procedure involving a "localization-planning-execution" cycle until a specified location of the car relative to its environment is reached. Range measurements are used to model environmental objects around the car. The automatic manoeuvres developed are demonstrated on an experimental electric autonomous car in a usual traffic environment.

Practical aspects of motion generation and control for parking a nonholonomic autonomous vehicle are considered. An iterative algorithm for the parallel parking maneuver is proposed. It is based on ultrasonic range data processing. To control the steering angle and longitudinal velocity of the vehicle during the parking maneuver, sinusoidal reference functions are used. To prevent collisions, the maneuver is carried out as a reactive motion. The developed control is experimentally verified for a LIGIER electric autonomous vehicle.

Path generation execution and motion control for an omnidirectional mobile robot (vehicle) are considered. Real-time path generation is based on the polynomial spline-interpolation with prediction of velocities of a spline-function. The mobile vehicle control is performed with the use of its kinematic model and digital PID-controllers of the wheels. The theoretical approach is illustrated by the obtained experimental results.>