Velocity Obstacle Research Articles

Guaranteed infinite horizon avoidance of unpredictable, dynamically constrained obstacles

This paper presents a new approach to guaranteeing collision avoidance with respect to moving obstacles that have constrained dynamics but move unpredictably. Velocity Obstacles have been used previously to plan trajectories that avoid collisions with obstacles under the assumption that the trajectories of the objects are either known or can be accurately predicted ahead of time. However, for real systems this predicted trajectory will typically only be accurate over short time-horizons. To achieve safety over longer time periods, this paper instead considers the set of all reachable points by an obstacle assuming that the dynamics fit the unicycle model, which has known constant forward speed and a maximum turn rate (sometimes called the Dubins car model). This paper extends the Velocity Obstacle formulation by using reachability sets in place of a single "known" trajectory to find matching constraints in velocity space, called Velocity Obstacle Sets. The Velocity Obstacle Set for each obstacle is equivalent to the union of all velocity obstacles corresponding to any dynamically feasible future trajectory, given the obstacle's current state. This region remains bounded as the time horizon is increased to infinity, and by choosing control inputs that lie outside of these Velocity Obstacle Sets, it is guaranteed that the host agent can always actively avoid collisions with the obstacles, even without knowing their exact future trajectories. Furthermore it is proven that, subject to certain initial conditions, an iterative planner under these constraints guarantees safety for all time. Such an iterative planner is implemented and demonstrated in simulation.

Velocity occupancy space: autonomous navigation in an uncertain, dynamic environment

In order to autonomously navigate in an unknown environment, a robotic vehicle must be able to sense obstacles, determine their velocities, and select a collision-free path that will lead quickly to a goal. However, the perceived location and motion of the obstacles will be uncertain due to the limited accuracy of the robot's sensors. Thus, it is necessary to develop a system that can avoid moving obstacles using uncertain sensor data. The method proposed here is based on an occupancy grid - which has been used to avoid stationary obstacles in an uncertain environment - in conjunction with velocity obstacles - which allow a robot to avoid well-known moving obstacles. The combination of these techniques leads to Velocity Occupancy Space (VOS): a search space which allows the robot to avoid moving obstacles and navigate efficiently to a goal using uncertain sensor data. The proposed method is validated by numerous simulation trials.

A Crowd Behavior Model Based on Reciprocal Velocity Obstacle Algorithm

A crowd behavior model based on reciprocal velocity obstacle algorithm is proposed in this paper. The model is the combination autonomous navigation and crowd behavior strategy module. Reciprocal Velocity Obstacle algorithm is adopted to achieve global path planning. individuals act quite differently under various circumstances, and several behavior strategies are applied in crowd simulation on the basis of social theory: in normal condition people show their humility confronting interaction, therefore they queue up with the application of stop rules; while in emergency circumstances, competition feature appears during inter-individual interactive process, as a result they decelerate towards their goals with the arching and congestion phenomena observed. The results show the good performance for crowd evacuation in real time and realism of the simulation process.

The Hybrid Reciprocal Velocity Obstacle

We present the hybrid reciprocal velocity obstacle for collision-free and oscillation-free navigation of multiple mobile robots or virtual agents. Each robot senses its surroundings and acts independently without central coordination or communication with other robots. Our approach uses both the current position and the velocity of other robots to compute their future trajectories in order to avoid collisions. Moreover, our approach is reciprocal and avoids oscillations by explicitly taking into account that the other robots sense their surroundings as well and change their trajectories accordingly. We apply hybrid reciprocal velocity obstacles to iRobot Create mobile robots and demonstrate direct, collision-free, and oscillation-free navigation.

Unified Approach of Unmanned Surface Vehicle Navigation in Presence of Waves

Most of the present work for unmanned surface vehicle (USV) navigation does not take into account environmental disturbances such as ocean waves, winds, and currents. In some scenarios, waves should be treated as special case of dynamic obstacle and can be critical to USV’s safety. For the first time, this paper presents unique concept facing this challenge by combining ocean waves' formulation with the probabilistic velocity obstacle (PVO) method for autonomous navigation. A simple navigation algorithm is presented in order to apply the method of USV’s navigation in presence of waves. A planner simulation dealing with waves and obstacles avoidance is introduced.

ANALYSIS OF CROWD WALKING USING EVACUATION BEHAVIOR FRAMEWORK AND APPLICATION TO EVACUATION SAFETY VERIFICATION

This study aims to evaluate evacuation safety using an evacuation behavior framework which deals several evacuation behavior models. In this paper, we implemented two crowd walking models, namely, the Social Force model and the RVO model. The Social Force model is a dynamical model which solves Newton's motion equation. On the other hand, the RVO model is an expanded model of velocity obstacles for collision avoidance. When we try to understand the characteristics of these models, the results are as follows. If the desired speed of the agents are over 3.5 m/s, the flow coefficient of the Social Force model declines because of increasing the frequency of arch actions. However the flow coefficient of the RVO model does not decline. As regards an application to the evacuation from a building, the flow of a room decline because of the crowded corridor.

Motion planning for mobile robots in dynamic environments using a potential field immune network

This paper proposes a potential field immune network (PFIN) for dynamic motion planning of mobile robots in an unknown environment with moving obstacles and a fixed/moving goal. The velocity obstacle method is utilized to determine the imminent collision obstacle. Subsequently, PFIN is implemented to guide robots to avoid collision with the most dangerous objects at every time instant. The overall response of the immune network is calculated with the aid of fuzzy logic and genetic algorithms. Simulation results are presented to verify the effectiveness of the proposed architecture in dynamic environments with single and multiple fixed/moving obstacles.

Real-time cooperative collision avoidance method of multiple mobile robots

In a previous study, the authors have proposed the Cooperative Collision Avoidance (CCA) method. The method extended the Velocity Obstacle to multiple mobile robot systems by introducing the concept of the Common Velocity Obstacle, and achieved cooperative collision avoidance among multiple mobile robot systems without supervisor or communication. The problem of the method was the enormous computation caused by the high dimensional search, and two improved CCA method are introduced in this paper. The first method transforms the search structure from a massive monolithic search into two separate light searches and this leads to significant reduction of the computation from O(a4) to O(a2), where a is the resolution of the search. The second method solves the search analytically by applying an assumption that every robot has a direction oriented objective. The effectiveness of the two methods are confirmed by the computer simulations.

Some Steps Towards Autonomous Cars

Developing new Intelligent Transportation Systems which take into consideration the social-economical, environmental, and safety factors of the modern human society, is one of the grand challenges of the 21th century. This paper presents the motion autonomy capabilities which have been developed for future cars in the scope of the French “Praxitele” and “Automated Road” projects. These new capabilities relies onto effective solutions that we have developed for contributing to solve three main problems: autonomous maneuvering for a car-like vehicle using the concept of Sensor-Based Maneuver, planning and controling the motions of a double-steered vehicle (the CyCab) using the concept of Differential Flatness, and obstacle avoidance in a dynamic environment using the concept of Non-Linear Velocity Obstacle. Experimental results obtained with real vehicles are also presented and discussed.

Motion Planning in Dynamic Environments Using Velocity Obstacles

This paper presents a method for robot motion planning in dynamic environments. It consists of selecting avoidance maneuvers to avoid static and moving obstacles in the velocity space, based on the cur rent positions and velocities of the robot and obstacles. It is a first- order method, since it does not integrate velocities to yield positions as functions of time. The avoidance maneuvers are generated by selecting robot ve locities outside of the velocity obstacles, which represent the set of robot velocities that would result in a collision with a given obstacle that moves at a given velocity, at some future time. To ensure that the avoidance maneuver is dynamically feasible, the set of avoidance velocities is intersected with the set of admissible velocities, defined by the robot's acceleration constraints. Computing new avoidance maneuvers at regular time intervals accounts for general obstacle trajectories. The trajectory from start to goal is computed by searching a tree of feasible avoidance maneuvers, computed at discrete time intervals. An exhaustive search of the tree yields near-optimal trajectories that either minimize distance or motion time. A heuristic search of the tree is applicable to on-line planning. The method is demonstrated for point and disk robots among static and moving obstacles, and for an automated vehicle in an intelligent vehicle highway system scenario.