Optimal Routing Schedules for Robots Operating in Aisle-Structures

In this paper, we consider the Constant-cost Orienteering Problem (COP) where a robot, constrained by a limited travel budget, aims at selecting a path with the largest reward in an aisle-graph. The aisle-graph consists of a set of loosely connected rows where the robot can change lane only at either end, but not in the middle. Even when considering this special type of graphs, the orienteering problem is known to be NP-hard. We optimally solve in polynomial time two special cases, COP-FR where the robot can only traverse full rows, and COP-SC where the robot can access the rows only from one side. To solve the general COP, we then apply our special case algorithms as well as a new heuristic that suitably combines them. Despite its light computational complexity and being confined into a very limited class of paths, the optimal solutions for COP-FR turn out to be competitive even for COP in both real and synthetic scenarios. Furthermore, our new heuristic for the general case outperforms state-of-art algorithms, especially for input with highly unbalanced rewards

Coverage Path Planning for a Moving Vehicle

A simple coverage plan called a Conformal Lawn Mower plan is demonstrated. This plan enables a UAV to fully cover the route ahead of a moving ground vehicle. The plan requires only limited knowledge of the ground vehicle's future path. For a class of curvature-constrained ground vehicle paths, the proposed plan requires a UAV velocity that is no more than twice the velocity required to cover the optimal plan. Necessary and sufficient UAV velocities, relative to the ground vehicle velocity, required to successfully cover any path in the curvature restricted set are established. In simulation, the proposed plan is validated, showing that the required velocity to provide coverage is strongly related to the curvature of the ground vehicle's path. The results also illustrate the relationship between mapping requirements and the relative velocities of the UAV and ground vehicle. Next, I investigate the challenges involved in providing timely mapping information to a moving ground vehicle where the path of that vehicle is not known in advance. I establish necessary and sufficient UAV velocities, relative to the ground vehicle velocity, required to successfully cover any path the ground vehicle may follow. Finally, I consider a reduced problem for sensor coverage ahead of a moving ground vehicle. Given the ground vehicle route, the UAV planner calculates the regions that must be covered and the time by which each must be covered. The UAV planning problem takes the form of an Orienteering Problem with Time Windows (OPTW). The problem is cast the problem as a Mixed Integer Linear Program (MILP) to find a UAV path that maximizes the area covered within the time constraints dictated by the moving ground vehicle. To improve scalability of the proposed solution, I prove that the optimization can be partitioned into a set of smaller problems, each of which may be solved independently without loss of overall solution optimality. This divide and conquer strategy allows faster solution times, and also provides higher-quality solutions when given a fixed time budget for solving the MILP. We also demonstrate a method of limited loss partitioning, which can perform a trade-off between improved solution time and a bounded objective loss

Study of new technological implications to improve food productivity and security in Ghana : case insights into the use of drones in cocoa farming

Since the early 1980’s, in developed countries such as Japan and the United States of America, several technological applications have been used experimentally to boost food production and enhance farming practices, especially in areas which are not geographically accessible for traditional farming practices and machineries.One such technology which has been extensively experimented with and deployed is the Unmanned Aerial Vehicle (UAV), which is an example of technological expertise pioneered by the military. Their growing adaptation in precision agriculture means that UAV have been used on farms in developed countries for crops grown on both small- and large land acreage for the purposes of identifying nutrient deficiencies, diseases, water and soil status, weeds, damage, and plant diagnostics.The study focuses on the adaptation and implementation of UAV in Ghana’s cocoa farming and the position of stakeholders in terms of their acceptance, as the country is currently the world’s second largest producer and exporter of cocoa. The study applies Disruptive Innovation theory and stakeholder theory as a joint conceptual framework by which to examine how new and long-established farms create, sustain, and continuously introduce creative and novel technology in order to maximise food production while assessing stakeholders’ attitudes and roles in the implementation of innovation.Conducted in Nkawie in the Ashanti region of Ghana, the study adopts a qualitative approach, using semi-structured interviews to elicit and collate the views of stakeholders on the implementation of UAV in cocoa farming in Ghana, ultimately analysing the resulting by use of NVivo software. The findings show that traditional practices and superstitious beliefs, lack of credit facilities can impede the acceptance of new innovation.The study identifies a comprehensive pool of stakeholders in the supply chain whose input significantly influences the implementation of UAV. Other key stakeholders maintained that limited support for local drone innovator community, access to funding, and corrupt practices hinder the implementation of this technology, although general awareness of its benefit to cocoa farming cannot be disputed. Despite the difficult conditions that arose during data collection due to COVID restrictions in the study area, 36 participant agreed to participate in the study through interviews. This study makes a specific contribution to the body of literature and policy framework on the drivers and barriers of UAV adoption and implementation in emerging economies such as Ghana in the cocoa farming industr