# Toward Sustainable Mars Exploration: A Perspective on Collaborative Intelligent Systems
## 5 Collaborative Energy-Aware Computing  
### Energy-Aware Task Scheduling and Distributed Computing  
In a scenario where a swarm of autonomous vehicles is deployed on Mars for exploration and colonization, a major challenge arises: Unlike on Earth, there is no readily available power supply distribution network on Mars. In this novel and challenging environment, the robotic workforce must swiftly establish both mobile and fixed charging stations to ensure their _survivability_[59]. To tackle this challenge, a swarm of intelligent autonomous vehicles can employ cutting-edge AI-based scheduling algorithms to assign tasks while ensuring secure energy availability across the fleet of highly mobile and precisely sensing rovers. For instance, drones equipped with AI capabilities can manage the optimal recharging of other vehicles' batteries through methods like wireless charging or battery replacement while partaking in the soil sampling tasks. As described in Section 2, each vehicle possesses a diverse set of capabilities, ranging from resource extraction to scientific exploration. Given the energy-intensive and unpredictable nature of tasks on Mars, along with the limited available resources, an autonomous, energy-aware dynamic task allocation system [60] can be deployed to efficiently manage task scheduling. This system autonomously allocates tasks based on real-time assessments of each vehicle's capabilities and available energy, ensuring efficient task execution while maintaining a secure energy flow among the ambiently powered vehicles. The autonomous nature of the task allocation system ensures that once task information is provided, it can perfectly handle the task execution without requiring continuous input from Earth. However, it is important to note that the Earth control system should monitor task accomplishments on a periodic basis, ensuring that the task's progress is aligned with mission objectives and verifying the successful completion of tasks and update the tasks list. This combination of autonomy and Earth-based monitoring ensures continuous, adaptive operations while accounting for the communication latency and disruptions inherent to Mars missions [61]. This approach involves the real-time assignment of tasks based on available resources, while being demonstrably resilient to robot failures and changes in environmental conditions [59]. The complex task allocation problem falls within the realm of online scheduling--a dynamic process requiring real-time adaptation of tasks to maximize efficiency. In the context of Martian exploration, efficiency refers to the ability to accomplish tasks and missions with minimal resource consumption, particularly in terms of time. The primary efficiency parameters revolve around minimizing the total time required to complete missions and tasks on Mars while adhering to constraints related to the availability of energy resources. To mitigate the risk of robotic failures, such as those experienced in NASA's earlier missions, one potential solution is to implement a redundant, low-power energy management module [62] among the collaborative robotic systems. This module would monitor efficient energy harvesting mechanisms, facilitate dead-battery robot recovery, and provide a hard reset capability for the robots. Additionally, incorporating low-power communication protocols, such as Zigbee or Bluetooth Mesh, could enhance coordination and data sharing among robots while minimizing energy consumption. Mathematically, metaheuristic algorithms are a feasible solution for addressing scheduling problems due to their ability to tackle NP-hard (non-deterministic polynomial-time hard) computational complexities. Despite the absence of a guarantee for globally optimized solutions, metaheuristic models have gained considerable attention in optimization domains [63]. This adoption can be attributed to their adeptness in an equilibrium between temporal efficiency and solution efficacy.