Multi-Robot
Search & Rescue.
Decentralized coordination for autonomous area coverage in dynamic disaster zones using Voronoi partitioning, repulsive fields, and energy-aware planning.
Coordinating a swarm
in a changing environment.
Search-and-rescue robots must explore efficiently while accounting for hazards, limited sensing and communication, and finite energy.
This project investigated a decentralized multi-robot swarm for search and rescue in a simulated disaster zone. The system used 20 robots operating in a 200 × 200 meter environment containing five dynamically expanding hazards. The objective was to maximize area coverage while allowing robots to make local decisions, avoid dangerous regions, and manage their energy consumption.
Local decisions.
Coordinated exploration.
The simulation combines local sensing, Voronoi-based region allocation, repulsive-field hazard avoidance, and energy-aware movement decisions. Each robot uses available information to determine its next action, while the collective behavior produces coordinated coverage of the environment.
Local Sensing
Robot position, nearby hazards, and available environment information.
Voronoi Regions
Distributed partitioning of the search area among robots.
Local Motion
Coverage objectives combined with hazard-repulsion behavior.
Energy Tracking
Movement decisions account for the robots' remaining energy.
Dividing the search
without a central planner.
Voronoi partitioning was used to divide the environment into regions associated with individual robots. This provides a distributed mechanism for assigning coverage responsibilities and reducing redundant exploration. Each robot can then select movement directions based on its local region and the surrounding environment.
Area Partitioning
Voronoi regions divide the search space into local areas associated with the robots.
Distributed Decisions
Robots make movement decisions locally instead of relying on a centralized controller.
Coverage Tracking
Coverage heatmaps and robot trajectories are used to evaluate exploration across the environment.
20200 × 200 mThe distributed approach allows the swarm to adapt its coverage behavior as robot positions and environmental conditions change, while operating under the sensing and communication constraints represented in the simulation.
Planning around
expanding danger zones.
The environment contained five dynamically expanding hazards. Repulsive fields were used to discourage robots from moving into dangerous regions, while gradient-based avoidance influenced their local trajectories. The resulting paths were analyzed alongside hazard maps to evaluate how the swarm responded to changes in the environment.
Hazard Growth
Five hazard regions expand dynamically during the simulation, changing the available search space.
Repulsive Fields
Hazards generate repulsive influences that guide robots away from dangerous regions.
Gradient-Based Avoidance
Robots adjust movement directions according to the combined coverage and avoidance behavior.
This approach makes hazard avoidance part of the coordination problem rather than treating it as a separate post-processing step. The simulation demonstrates how local avoidance behavior can be combined with distributed area coverage.
Exploration with
a finite energy budget.
Each robot's energy was tracked throughout the mission, with energy depletion applied at each simulation step. The planning strategy accounted for remaining energy while robots continued exploring their assigned regions and avoiding hazards.
Remaining energy at mission end, as reported in the simulation.
Coverage without exhausting the swarm.
Energy consumption was incorporated into the mission evaluation rather than considering coverage alone. This provides a more practical basis for studying long-duration multi-robot missions.
Measuring collective
mission performance.
The MATLAB simulation demonstrated coordinated exploration with decentralized decision-making, dynamic hazard avoidance, and energy-aware movement. The reported results provide a quantitative view of coverage and resource use within the simulated environment.
20-Robot Swarm
Twenty simulated robots coordinated to explore the 200 × 200 meter disaster environment.
>95% Coverage
The swarm achieved more than 95% total area coverage within 250 simulation steps.
>80% Energy
Robots retained more than 80% of their energy by the end of the simulated mission.
>95%250 steps5>80%See the swarm
in motion.
The simulation demonstrates the robots' coverage behavior, trajectory evolution, and response to dynamically expanding hazards.
Decentralized Swarm Simulation
MATLAB visualization of coordinated area coverage, robot trajectories, and dynamic hazard avoidance.
Explore the
implementation.
View the source repository and the complete project report for implementation details and further results.
Have a robotics
challenge in mind?
I'm interested in opportunities involving autonomous systems, robotics software, controls, simulation, and real-world deployment. If you're building something ambitious, I'd be glad to connect.
dheerajcnv@gmail.com