We present an error tolerant path planning algorithm for Micro Aerial Vehicles (MAV) swarms. We assume a MAV navigation system without relying on GPS-like techniques. The MAV find their navigation path by using their sensors and cameras, in order to identify and follow a series of visual landmarks. The visual landmarks lead the MAV towards the target destination. MAVs are assumed to be unaware of the terrain and locations of the landmarks. Landmarks are also assumed to hold a-priori information, whose interpretation (by the MAVs) is prone to errors. We distinguish two types of errors, namely, recognition and advice. Recognition errors are due to misinterpretation of sensed data and a-priori information or confusion of objects (e.g., due to faulty sensors). Advice errors are due to outdated or wrong information associated to the landmarks (e.g., due to weather conditions). Our path planning algorithm proposes swarm cooperation. MAVs communicate and exchange information wirelessly, to minimize the recognition and advice error ratios. By doing this, the navigation system experiences a quality amplification in terms of error reduction. As a result, our solution successfully provides an adaptive error tolerant navigation system. Quality amplification is parametetrized with regard to the number of MAVs. We validate our approach with theoretical proofs and numeric simulations.
A fundamental aspect of autonomous vehicle guidance is planning trajectories. Historically, two fields have contributed to trajectory or motion planning methods: robotics and dynamics and control. The former typically have a stronger focus on computational issues and real-time robot control, while the latter emphasize the dynamic behavior and more specific aspects of trajectory performance. Guidance for Unmanned Aerial Vehicles (UAVs), including fixed- and rotary-wing aircraft, involves significant differences from most traditionally defined mobile and manipulator robots. Qualities characteristic to UAVs include non-trivial dynamics, three-dimensional environments, disturbed operating conditions, and high levels of uncertainty in state knowledge. Otherwise, UAV guidance shares qualities with typical robotic motion planning problems, including partial knowledge of the environment and tasks that can range from basic goal interception, which can be precisely specified, to more general tasks like surveillance and reconnaissance, which are harder to specify. These basic planning problems involve continual interaction with the environment. The purpose of this paper is to provide an overview of existing motion planning algorithms while adding perspectives and practical examples from UAV guidance approaches. Unmanned aerial vehicles (UAVs) have recently attracted the attention of researchers due to their numerous potential civilian applications. However, current robot navigation technologies need furth...   Potential-field-based control strategy for path planning and formation control of multi Quadrotor systems is proposed in this work. The potential field is used to attract the Quadrotor to the goal location as well as avoiding the obstacle. The algorithm to solve the so called local minima problem by utilizing the wall-following behavior is also proposed. The resulted path planning via potential function strategy is then used to design formation control algorithm. Using the virtual leader approach, the formation control strategy by means of potential function is formulated. Each Quadrotor is assigned attractive potential to reach its desired position in formation. Each Quadrotor is also treated as moving obstacle which induces repulsive potential to the others in order to prevent inter-robot collision. The desired position for each Quadrotor in certain configuration is calculated based on the virtual leaderâ��s current position. To get smooth movement, the virtual leader is set as unicycle robot. The repulsive potential is also attached to each agent in case it moves in the region inside the obstacleâ��s influence. The overall strategy has been successfully applied to the Quadrotorâ��s model of Parrot AR Drone 2.0 in Gazebo simulator programmed using Robot Operating System. In this paper, we consider the problem of concurrent assignment and planning of trajectories (which we denote Capt) for a team of robots. This problem involves simultaneously addressing two challenges: (1) the combinatorially complex problem of finding a suitable assignment of robots to goal locations, and (2) the generation of collision-free, time parameterized trajectories for every robot. We consider the Capt problem for unlabeled (interchangeable) robots and propose algorithmic solutions to two variations of the Capt problem. The first algorithm, c-Capt, is a provably correct, complete, centralized algorithm which guarantees collision-free optimal solutions to the Capt problem in an obstacle-free environment. To achieve these strong claims, c-Capt exploits the synergy obtained by combining the two subproblems of assignment and trajectory generation to provide computationally tractable solutions for large numbers of robots. We then propose a decentralized solution to the Capt problem through d-Capt, a decentralized algorithm that provides suboptimal results compared to c-Capt . We illustrate the algorithms and resulting performance through simulation and experimentation. This paper presents a unique real-time obstacle avoidance approach for manipulators and mobile robots based on the artificial potential field concept. Collision avoidance, tradi tionally considered a high level planning problem, can be effectively distributed between different levels of control, al lowing real-time robot operations in a complex environment. This method has been extended to moving obstacles by using a time-varying artificial patential field. We have applied this obstacle avoidance scheme to robot arm mechanisms and have used a new approach to the general problem of real-time manipulator control. We reformulated the manipulator con trol problem as direct control of manipulator motion in oper ational space—the space in which the task is originally described—rather than as control of the task's corresponding joint space motion obtained only after geometric and kine matic transformation. Outside the obstacles' regions of influ ence, we caused the end effector to move in a straight line with an...