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Venue comes from the Latin word meaning ‘come’ (venire). Not only the event's location or venue affect attendance but it can also have an impact on the event's personality. Additionally, the location or venue affects the event's atmosphere and the visitor experience. The most significant benefit of revitalisation or adaptive reuse is its positive impact on the environment as razing buildings is a major source of waste and carbon emissions, by contrast, adaptive reuse reduces waste, saves energy and conserves resources. The persistence of this chapter is to investigate the challenges and opportunities in making adaptive re-use or revitalisation of heritage buildings that includes forts/palace/museums as a venue for any event. For the purpose of achieving the goal, secondary data from 2000 to 2023 have been compiled from more than 50 research articles that have been published in peer-reviewed and University Grant Commission (UGC) journals, books written by various authors, including the authors themselves, other researchers working in the related field of event management, conference proceedings and websites. The authors discovered from their examination of the literature that the significance of the event venue cannot be unheeded. The event's tone sets the stage for possible guest experience influences. One must carefully consider the kind of venues or settings that are available in the destination before conducting any event.

Visual feedback control is a promising solution for robots work in unstructured environments, and this is accomplished by estimation of the time derivative relationship between the image features and the robot moving. While some of the drawbacks associated with most visual servoing (VS) approaches include the vision–motor mapping computation and the robots’ dynamic performance, the problem of designing optimal and more effective VS systems still remains challenging. Thus, the purpose of this paper is to propose and evaluate the VS method for robots in an unstructured environment.

This paper presents a new model-free VS control of a robotic manipulator, for which an adaptive estimator aid by network learning is proposed using online estimation of the vision–motor mapping relationship in an environment without the knowledge of statistical noise. Based on the adaptive estimator, a model-free VS schema was constructed by introducing an active disturbance rejection control (ADRC). In our schema, the VS system was designed independently of the robot kinematic model.

The various simulations and experiments were conducted to verify the proposed approach by using an eye-in-hand robot manipulator without calibration and vision depth information, which can improve the autonomous maneuverability of the robot and also allow the robot to adapt its motion according to the image feature changes in real time. In the current method, the image feature trajectory was stable in the camera field range, and the robot’s end motion trajectory did not exhibit shock retreat. The results showed that the steady-state errors of image features was within 19.74 pixels, the robot positioning was stable within 1.53 mm and 0.0373 rad and the convergence rate of the control system was less than 7.21 s in real grasping tasks.

Compared with traditional Kalman filtering for image-based VS and position-based VS methods, this paper adopts the model-free VS method based on the adaptive mapping estimator combination with the ADRC controller, which is effective for improving the dynamic performance of robot systems. The proposed model-free VS schema is suitable for robots’ grasping manipulation in unstructured environments.