Presence Of Dynamic Obstacles Research Articles

Integrated motion planning and control for graceful balancing mobile robots

This paper presents an integrated motion planning and control framework that enables balancing mobile robots to gracefully navigate human environments. A palette of controllers called motion policies is designed such that balancing mobile robots can achieve fast, graceful motions in small, collision-free domains of the position space. The domains determine the validity of a motion policy at any point in the robot’s position state space. An automatic instantiation procedure that generates a motion policy library by deploying motion policies from a palette on a map of the environment is presented. A gracefully prepares relationship that guarantees valid compositions of motion policies to produce overall graceful motion is introduced. A directed graph called the gracefully prepares graph is used to represent all valid compositions of motion policies in the motion policy library. The navigation tasks are achieved by planning in the space of these gracefully composable motion policies. In this work, Dijsktra’s algorithm is used to generate a single-goal optimal motion policy tree, and its variant is used to rapidly replan the optimal motion policy tree in the presence of dynamic obstacles. A hybrid controller is used as a supervisory controller to ensure successful execution of motion policies and also successful switching between them. The integrated motion planning and control framework presented in this paper was experimentally tested on the ballbot, a human-sized dynamically stable mobile robot that balances on a single ball. The results of successful experimental testing of two navigation tasks, namely, point-point and surveillance motions are presented. Additional experimental results that validate the framework’s capability to handle disturbances and rapidly replan in the presence of dynamic obstacles are also presented.

Predictive Guidance-Based Navigation for Mobile Robots: A Novel Strategy for Target Interception on Realistic Terrains

This paper presents a novel online trajectory planning method for the autonomous robotic interception of mobile targets in the presence of dynamic obstacles. The objective is time-optimal position and velocity matching (also referred to as rendezvous) while traversing realistic terrains with uneven topologies. The primary novelty of the proposed interception method lies in its ability to minimize rendezvous time with the target, as well as energy consumption, by directly considering the dynamics of the obstacles and the target while accurately determining a feasible way to travel through the realistic terrain. This objective is achieved by computing rendezvous maneuvers using an advanced predictive guidance law. The method is designed to effectively cope with maneuvering targets/obstacles by predicting their future velocities and accelerations. Obstacle avoidance and terrain navigation are seamlessly integrated. Extensive simulation and experimental analyses, some of which are reported in this paper, have clearly demonstrated the time efficiency of the proposed rendezvous method.

Advanced Predictive Guidance Navigation for Mobile Robots: A Novel Strategy for Rendezvous in Dynamic Settings

Abstract This paper presents a novel on-line trajectory planning method for the autonomous robotic interception of moving targets in the presence of dynamic obstacles, i.e., position and velocity matching (also referred to as rendezvous). The novelty of the proposed time-optimal interception method is that it directly considers the dynamics of the obstacles as well as the target in its interception maneuver: the velocities and accelerations of the obstacles and the target are predicted in real-time for potential collisions. The method is designed to deal with highly- maneuvering obstacles and targets. The interception maneuver is computed using an Advanced Predictive Guidance Law. Extensive simulation and experimental analyses, some of which are reported in this paper, have clearly demonstrated the time efficiency of the proposed rendezvous method.

Rendezvous-Guidance Trajectory Planning for Robotic Dynamic Obstacle Avoidance and Interception

This correspondence presents a novel online trajectory-planning method for the autonomous robotic interception of moving targets in the presence of dynamic obstacles, i.e., position and velocity matching (also referred to as rendezvous). The proposed time-optimal interception method is a hybrid algorithm that augments a novel rendezvous-guidance (RG) technique with the velocity-obstacle approach, for obstacle avoidance, first reported by Fiorini and Shiller. The obstacle-avoidance algorithm itself could not be used in its original form and had to be modified to ensure that the online planned path deviates minimally from the one generated by the RG algorithm. Extensive simulation and experimental analyses, some of which are reported in this correspondence, have clearly demonstrated the tangible time efficiency of the proposed interception method.

A Neural Controller for Mobile Robots

A neural controller for visually guided navigation of a mobile robot in the presence of dynamic obstacles and a vector field of attraction forces is presented. Discrete information, obtained from a raster-type sensor, is subjected to parallel distributed processing aiming at real-time detection of geometrical properties of visual representations of the obstacles. A characteristic feature of the neural architecture adopted here is processing of the input data by a hidden layer of dipole units. This novel approach allows the controller to perform the operations of edge detection and recognition of geometrical features within the controller’s structure rather than using visual data pre-processed by classical convolution-based methods. Dipole processing combined with the ability of our network to learn arbitrary associations very rapidly by self-programming permits the processing of large amounts of input data without compromising the efficiency of the learning phase. The capability of the network to extract and process some geometrical characteristics of the environment works for both local (limited by a field of view window) and global navigation. The behavior of the robot in the vicinity of obstacles can be tuned and modified by changing the number of recursively generated obstacle hulls, the size of the window, and the weights of the force components intervening in the control.