We propose new redundancy-based solutions to avoid robot joint limits of a manipulator. We use a control scheme based on the task function approach. We first recall the classical gradient projection approach and then present a far more efficient method that relies on the iterative computation of motion that does not affect the task achievement and ensures the avoidance problem. We apply this new method in a visual servoing application. We demonstrate the validity of the approach on various real experiments as well as on the control of a virtual humanoid.

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A redundancy-based iterative approach for avoiding joint limits: application to visual servoing

Robotics is a fast developing and exciting field with multiple applications which span from robot manipulators to mobile robots. This paper presents a literature review on wheeled mobile robots. We point out the main considerations to be taken into account when designing this class of robots. The main control problems are recalled as well as the corresponding solutions. Finally, we summarize the most important approaches used to render mobile robots autonomous.

Wheeled Mobile Robots: A review

This paper presents a new hybrid control approach for vision-based navigation applied to autonomous robotic automobiles in urban environments. It is composed by a visual servoing (VS) for road lane following (as deliberative control) and a dynamic window approach (DWA) for obstacle avoidance (as reactive control). Typically, VS applications do not change the velocities to stop the robot in dangerous situations or avoid obstacles while performing the navigation task. However, in several urban conditions, these are elements that must be dealt with to guarantee the safe movement of the car. As a solution for this problem, in this paper, a line-following VS controller will be used to perform road lane following tasks with obstacle avoidance, validating its control outputs in a new image-based dynamic window approach (IDWA). The final solution combines the benefits of both controllers (VS+IDWA) for optimal lane following and fast obstacle avoidance, taking into account the car kinematics and some dynamics constraints. Experiments in a challenging scenario with both simulated and real experimental car show the viability of the proposed methodology.

https://hal.archives-ouvertes.fr/hal-01305733/document

A Hybrid Controller for Vision-Based Navigation of Autonomous Vehicles in Urban Environments

This paper presents a sensor-based controller allowing to visually drive a mobile robot towards a target while avoiding visual features occlusions and obstacle collisions. We consider the model of a cart-like robot equipped with proximetric sensors and a camera mounted on a pan-platform. The proposed method relies on the continuous switch between three controllers realizing respectively the nominal vision-based task, the obstacle by passing and the occlusion avoidance. Simulation results are given at the end of the paper.

/pdf/a-controller-to-avoid-both-occlusions-and-obstacles-during-a-570jn8xzke.pdf

A controller to avoid both occlusions and obstacles during a vision-based navigation task in a cluttered environment

Presents two control strategies based on the data provided by a camera and a 2D laser range sensor for driving a mobile robot towards a target amidst obstacles. The methods are based on the task function formalism and use the robot redundancy with respect to the tracking task to control both the base and the camera during the avoidance phase. Simulation results are given at the end of the paper.

Mixed camera-laser based control for mobile robot navigation

Presents a controller for driving a mobile robot towards a target in a cluttered environment The proposed method combines visual servoing techniques allowing the target to be tracked, with an obstacle avoidance strategy based on the information provided by a 2D laser range sensor Simulation results are presented at the end of the paper

M. Devy

Papers

A controller to perform a visually guided tracking task in a cluttered environment