One of the most critical applications undertaken by coalitions of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) is reaching predefined targets by following the most time-efficient routes while avoiding collisions. Unfortunately, UAVs are hampered by limited battery life, and UGVs face challenges in reachability due to obstacles and elevation variations. Existing literature primarily focuses on one-to-one coalitions, which constrains the efficiency of reaching targets. In this work, we introduce a novel approach for a UAV-UGV coalition with a variable number of vehicles, employing a modified mean-shift clustering algorithm to segment targets into multiple zones. Each vehicle utilizes Multi-agent Deep Deterministic Policy Gradient (MADDPG) and Multi-agent Proximal Policy Optimization (MAPPO), two advanced reinforcement learning algorithms, to form an effective coalition for navigating obstructed environments without collisions. This approach of assigning targets to various circular zones, based on density and range, significantly reduces the time required to reach these targets. Moreover, introducing variability in the number of UAVs and UGVs in a coalition enhances task efficiency by enabling simultaneous multi-target engagement. The results of our experimental evaluation demonstrate that our proposed method substantially surpasses current state-of-the-art techniques, nearly doubling efficiency in terms of target navigation time and task completion rate.

Tom is also a co-inventor of an advanced 3D flight navigation algorithm for drones which is being utilised in new software applications for Aerologix. Tom guest lecturers at one of Australia's top universities – The University of New South Wales, teaching subject matter on Unmanned flight, he also holds a position on a government subcommittee dedicated to developing rules and regulations for unmanned aerial vehicles. Tom's passion for disruptive technology is infections, he is always looking for new challenges, especially drone tech and IoT. Tom has a very successful track record of establishing, executing and delivering large complex technical projects, Tom recently set up the largest drone network in Australia to monitor 1700 km of coastline to enhance swimmer safety. Tom enjoys complex problem solving and welcomes the challenge of empowering team members and creating new innovative ways to solve real-world problems. He has a high passion for life and enjoys a healthy lifestyle, and loves adventure sports such as kitesurfing, mountain biking when time permits.

The animated face of prototype robot GRACE, Graduate Robot Attending Conference, is tested by Carnegie Mellon University computer scientist Reid Simmons, right, in the lab at the school in Pittsburgh Tuesday, July 9, 2002. It may have been the first bit of fake news in the history of the Internet: in 1984, someone posted on Usenet that the Soviet Union was joining the network. It was a harmless April's Fools Day prank, a far cry from today's weaponized disinformation campaigns and unscrupulous fabrications designed to turn a quick profit. In 2017, misleading and maliciously false online content is so prolific that we humans have little hope of digging ourselves out of the mire. Instead, it looks increasingly likely that the machines will have to save us.