Physicomimetics for Swarm Formations and Obstacle Avoidance

Physicomimetics for Swarm Formations and Obstacle Avoidance Suranga Hettiarachchi Ph.D. Computer Science and Multimedia Eastern Oregon University Funded by Joint Ground Robotics Enterprise - DOD

Focus of the Talk • Improved performance in swarm obstacle avoidance: • Scales to far higher numbers of robots and obstacles than the norm • Hardware Implementation • Implemented obstacle avoidance algorithm on real robots Obstacle Avoidance Simulated Robot Swarms Hardware Implementation

Outline • Robot Swarms • Physicomimetics Framework • Swarm Learning • Obstacle Avoidance with Physical Robots • Conclusion and Future Work

Robot Swarms • Robot swarms can act as distributed computers, solving problems that a single robot cannot • For many tasks, having a swarm maintain cohesiveness while avoiding obstacles and performing the task is of vital importance • Example :Chemical Plume Source Tracing picture: Maxelbots at UW-DRL

Swarm Advantages • Swarms of robots are effective: • They can perform tasks that one expensive robot cannot. • Example: UAVs for surveillance • Swarms are robust: • Even if some robots fail, the swarm can still achieve the task. • Robots can be reused: • Functionally specific agents can be used to solve different problems picture: global aircraft

Physicomimetics for Robot Control • Biomimetics: Gain inspiration from biological systems and ethology. • Physicomimetics: Gain inspiration from • physical systems. Good for formations.

Why we mimic physics? • Aggregate behaviors seen in classical physics is potentially reproducible with collections of mobile agent. • Incorporate our understanding of classical physics to derive collective behavior of robots. • We are not restricted to copying physics precisely, so modifications are possible.

Physicomimetics Framework Robots have limited sensor range, and friction for stabilization Robots are controlled via “virtual” forces from nearby robots, goals, and obstacles. F = ma control law. Seven robots form a hexagon

Two Classes of Force Laws The “classic” law Novel use of LJ force law for robot control The left “Newtonian” force law, is good for creating swarms in rigid formations. The right “Lennard-Jones” force law (LJ) more easily models fluid behavior, which is potentially better for maintaining cohesion while avoiding obstacles.

What do these force laws look like? Change in Force Magnitude With Varying Distance for Robot – Robot Interactions Fmax = 1.0 Fmax = 4.0 Desired Robot Separation Distance = 50

Swarm Learning • Typically, the interactions between the swarm members are learned via simulation. Swarm Simulation Rules Fitness Final Rules that achieve the desired behavior Evolutionary Algorithm (EA) Initial Rules

Swarm Simulation Environment

Learning Approach • An Evolutionary Algorithm (EA) is used to evolve the rules for the robots in the swarm. • A global observer assigns fitness to the rules based on the collective behavior of the swarm in the simulation. • Each member of the swarm uses the same rules. The swarm is a homogeneous distributed system. • For physicomimetics, the rules are vectors of force law parameters.

Force Law Parameters • Parameters of the “Newtonian” force law G- “gravitational” constant of robot-robot interactions P- power of the force law for robot-robot interactions Fmax- maximum force of robot-robot interactions Similar 3-tuples for obstacle/goal-robot interactions. • Parameters of the LJ force law ε- strength of the robot-robot interactions c- non-negative attractive robot-robot parameter d- non-negative repulsive robot-robot parameter Fmax- maximum force of robot-robot interactions Similar 4-tuples for obstacle/goal-robot interactions.

Measuring Fitness • Connectivity (Cohesion) : maximum number of robots connected via a communication path. • Reachability (Survivability) : percentage of robots that reach the goal. • Time to Goal : time taken by at least 80% of the robots to reach the goal. High fitness corresponds to high connectivity, high reachability, and low time to goal. goal connectivity 4R reachability

Connectivity of Robots

Time for 80% of the Robots to Reach the Goal

Summary of Results • We compared the performance of the best Newtonian force law found by the EA to the best LJ force law. • The “Newtonian” force law produces more rigid structures making it difficult to navigate through obstacles. This causes poor performance, despite high connectivity. • LJ is superior, because the swarm acts as a viscous fluid. Connectivity is maintained while allowing the robots to reach the goal in a timely manner. • The LJ force law demonstrates scalability in the number of robots and obstacles.

Obstacle Avoidance with Robots • Use three Maxelbot robots • Use 2D trilateration localization algorithm (Not a part of this talk) • Design and develop obstacle avoidance module (OAM) • Implement physicomimetics on a real outdoor robot

Hardware Architecture of Maxelbot MiniDRAGON for trilateration, provides robot coordinates RF and acoustic sensors I2C MiniDRAGON for motor control, executes Physicomimetics OAM AtoD conversion I2C I2C IR sensors

Formation Control Methodology • Measure the quality of AP-lite without repulsions from obstacles • All experiments are conducted outdoor • Three Maxelbots: One leader and two followers • Results averaged over five runs • Leader remotely controlled (NO AP-lite) • Robots DO NOT have obstacle avoidance capability • Focus is on the formation control, not the obstacle avoidance

Why AP-lite? • Capable of maintaining formations of robots • Designed as a leader-follower algorithm • Allows robots to move quickly, due to minimal communication • Can use theory to set parameters

Triangular Formation

Linear Formation

Physicomimetics for Obstacle Avoidance • Constant “virtual” attractive goal force in front of the leader • “Virtual” repulsive forces from four sensors mounted on the front of the leader, if obstacles detected • The resultant force creates a change in velocity due to F = ma • Power supply to motors are changed based on the forces acting on the leader.

Obstacle Avoidance Methodology • Measure the performance of physicomimetics with repulsion from obstacles • All experiments are conducted outdoor • Three Maxelbots: One leader and two followers • Graphs show the correlation between raw sensor readings and motor power • Leader uses the physicomimetics algorithm with the obstacle avoidance module • Focus is on the obstacle avoidance by the leader, not the formation control

If there is an obstacle on the right, power to left motor is reduced

If there is an obstacle in front, power to both motors is reduced

Further Analysis of Sensor Reading and Motor Power • Scatter plots show how much one variable is affected by the other • Provide a broader picture of change in motor power when the robot sensors detects obstacles • Shows the correlation of motor power with distance to an obstacle in inches (the robots ignore obstacles greater than 30” away)

Lag in stopping due to AP inertia. Helps counteract noisy sensors. Right sensor sees obstacle Lag in starting due to AP inertia. Helps counteract noisy sensors. Right middle sensor sees obstacle

Power will be reduced if the outermost sensors see an obstacle when the inner sensors do not.

Future Work • Provide obstacle avoidance capability to all the robots in the formation • Develop robots with greater data exchange capability • Adapt the physicomimetics framework to incorporate performance feedback for specific tasks and situational awareness • Extend the physicomimetics framework for sensing and performing tasks in a marine environment (with Harbor Branch) • Introduce robot/human roles and interactions to distributed evolution architecture

Thank YouQuestions? Movie of 3 Maxelbots, Leader has OAM

Physicomimetics for Swarm Formations and Obstacle Avoidance