This paper presents a novel image-based path-planning and execution framework for vision-based control of a robot in a human centered environment. The proposed method involves applying Rapidly-exploring Random Tree (RRT) exploration to perform Image-Based Visual Servoing (IBVS) while satisfying multiple task constraints by exploiting robot redundancy. The methodology incorporates data-set of robot's workspace images for path-planning and design a controller based on visual servoing framework. This method is generic enough to include constraints like Field-of-View (FoV) limits, joint limits, obstacles, various singularities, occlusions etc. in the planning stage itself using task function approach and thereby avoiding them during the execution. The use of path-planning eliminates many of the inherent limitations of IBVS with eye-in-hand configuration and makes the use of visual servoing practical for dynamic and complex environments. Several experiments have been performed on a UR5 robotic manipulator to demonstrate that it is an effective and robust way to guide a robot in such environments.

In this paper, the problem of path planning through tight spaces, for the task of spatial payload transportation, using a formation of mobile manipulators is addressed. Due to the high dimensional configuration space of the system, efficient and geometrically stable path planning through tight spaces is challenging. We resolve this by planning the path for the system in two phases. First, an obstacle-free trajectory in R3for the payload being transported is determined using RRT. Next, near-energy optimal and quasi-statically stable paths are planned for the formation of robots along this trajectory using non-linear multi-objective optimization. We validate the proposed approach in simulation experiments and compare different multi-objective optimization algorithms to find energy optimal and geometrically stable robot path plans.

Objects in space often exhibit a tumbling motion around the major inertial axis. In this paper, we address the image based visual servoing of a robotic system towards an uncooperative tumbling object. In contrast to previous approaches that require explicit reconstruction of the object and an estimation of its velocity, we propose a novel controller that is able to minimize the feature error directly in image space. This is achieved by observing that the feature points on the tumbling object follow a circular path around the axis of rotation and their projection creates an elliptical track in the image plane. Our controller minimizes the error between this elliptical track and the desired features, such that at the desired pose the features lie on the circumference of the ellipse. The effectiveness of our framework is exhibited by implementing the algorithm in simulation as well on a mobile robot.

At the cross section of the fields of Uneven Terrain Navigation and Multi Agent Systems (MAS), in this work, a Detachable Compliant Modular Robot (DCMR) which can perform concurrent scene exploration by detaching into numerous parts, while preserving its ability to climb stairs is proposed and built. A spring is designed and used in the modular robot taking the worst-case-scenario of stairs encountered in an urban setting. In addition to the actuators at the wheels, an additional set of actuators per module are introduced to enable the detachment and re-attachment. The design additions and their trade-offs are discussed. Potential applications are presented with special focus on improving coverage of a map with obstacles/slabs large enough to merit exploration by climbing them. The problem of turning in crammed spaces is solved using the ability to detach of DCMR. The detaching & re-attaching capability, and stair climbing of the composite modular robot are demonstrated through experimentation using the prototype.

This work addresses path planning for reactionless visual servoing of a redundant dual-arm space robot through exploration in the image space. The planner explores the image moment based feature space, impends acceleration to the image features and extends the feature tree. A reactionless visual servoing control law is integrated to extend the tree in the configuration space simultaneously. The proposed algorithm is able to incorporate the necessary coupling between the motions of the the dual arms and the base of the robot to ensure zero base reactions. Additionally, it also gives the flexibility to apply multiple constraints in both the image space and the configuration space. The effectiveness of the proposed framework is exhibited by implementing the algorithm on a numerical model of a 14-DoF dual arm space robot.

Stair Climbing is a key functionality desired for robots deployed in Urban Search and Rescue (USAR) scenarios. A novel compliant modular robot was proposed earlier to climb steep and big obstacles. This work extends the functionality of this robot to ascend and descend stairs of dimensions that are also typical of an urban setting. Stair Climbing is realized by equipping the robot's link joints with optimally designed passive spring pairs that resist clockwise and counter clockwise moments generated by the ground during the climbing motion. This 3-module robot is only propelled by wheel actuators. Desirable stair climbing configurations are estimated a-priori and used to obtain the optimal stiffness for springs. Extensive numerical simulation results over different stair configurations are shown. The numerical simulations are corroborated by experimentation using the prototype and its performance is tabulated for different types of surfaces.

A novel compliant robot is proposed for traversing on unstructured terrains. The robot consists of modules, each containing a link and an active wheel-pair, and neighboring modules are connected using a passive joint. This type of robots are lighter and provide high durability due to the absence of link-actuators. However, they have limited climbing ability due to tendency of tipping over while climbing big obstacles. To overcome this disadvantage, the use of compliant joints is proposed in this work. Stiffness of each compliant joint is estimated by formulating an optimization problem with an objective to minimize link joint moments while maintaining static-equilibrium. This is one of the key novelties of the proposed work. A design methodology is also proposed for developing an n-module compliant robot for climbing a given height on a known surface. The efficacy of the proposed formulation is illustrated using numerical simulations of the three and five module robots. The robot is successfully able to climb maximum heights upto three times and six times the wheel diameter using three and five modules, respectively. A working prototype was developed and the simulation results were successfully validated on it.

This paper presents a novel visual servoing controller for a satellite mounted dual-arm space robot. The controller is designed to complete the task of servoing the robot's endeffectors to the desired pose, while regulating orientation of the base-satellite. Task redundancy approach is utilized to coordinate the servoing process and attitude of the base satellite. The visual task is defined as a primary task, while regulating attitude of the base satellite to zero is defined as a secondary task. The secondary task is formulated as an optimization problem in such a way that it does not affect the primary task, and simultaneously minimizes its cost function. A set of numerical experiments are carried out on a dual-arm space robot showing efficacy of the proposed control methodology.