Intention-Aware Navigation in Crowds with Extended-Space POMDP Planning

Himanshu Gupta

Bradley Hayes

Zachary Sunberg

Autonomous systems in the real world

A capable robot must

Infer pedestrian's intention

Predict pedestrian's behavior given its intention

Plan a path
to its goal location 

Prior Work

Reactive Controller

Predict and Act Controller

Issues

  • Pedestrian Model?
  • Future effect of immediate actions?

Issue

  • Uncertainty in pedestrian intention estimation?

Need a method that determines the optimal action for the vehicle at any given time by reasoning over the uncertainty in pedestrian intention estimation?
 

Need a method that determines the optimal action for the vehicle at any given time by reasoning over the uncertainty in pedestrian intention estimation?
 

Two-step Approach

Bai et. al, ICRA 2015

Two-step Approach

Bai et. al, ICRA 2015

Solving POMDP using DESPOT

  • STATE:
    \((x_c,y_c,\theta_c,v_c, g_c)\)
    corresponding to the 2D pose, speed and goal of the vehicle.
    \((x_i,y_i,v_i, g_i)\)
    corresponding to the \(i^{th}\) pedestrian's state
  • ACTION:
    $$\delta_s \in \{\textbf{Increase Speed, Decrease Speed,}$$ $$\textbf{Maintain Speed, Sudden Brake\}}$$

Effective
Roll-out Policy is important

Bai et. al, ICRA 2015

Two-step Approach

$$\delta_s \in \{\textbf{Increase Speed, Decrease Speed,}$$ $$\textbf{Maintain Speed, Sudden Brake\}}$$

Bai et. al, ICRA 2015

\(\textbf{1D-A}^*\) Approach

ISSUES?
 

  • Decoupling of heading angle planning and speed planning often leads to unnecessary stalling of the vehicle!


     
  • Hybrid A* path can't be found at at every time step!

Bai et. al, ICRA 2015

2D Approach

2D Approach

Solving POMDP using DESPOT

  • STATE:
    \((x_c,y_c,\theta_c,v_c, g_c)\)
    corresponding to the 2D pose, speed and goal of the vehicle.
    \((x_i,y_i,v_i, g_i)\)
    corresponding to the \(i^{th}\) pedestrian's state
  • ACTION:
    $$\delta_s(t) \in \{\textbf{Increase Speed, Decrease Speed,}$$ $$\textbf{Maintain Speed, Sudden Brake\}}$$

Same as previous POMDP

  • ACTION:

    $$\mathcal{a = ( \delta_\theta ,  \delta_s )}$$

2D Approach

  • Critical Challenge:  Determining a good roll-out policy for the vastly increased set of states reachable in the tree search.

Effective roll-out policy

  • Obtain a path using multi query motion planning technique
    • Probabilistic RoadMap (PRM)
    • Fast Marching Method (FMM)

       
  • Roll-out policy: Execute a reactive controller over the obtained path

Probabilistic RoadMaps (PRM) for Multi-Query Path Planning

Simulation Environment

  • Environment: \(100\)m x \(100\)m square field
  • Autonomous vehicle: A holonomic vehicle.
    • Inspired by Kinova MOVO
    • Max speed: \(2\) m/s

Experimental Scenarios

Scenario 1
(Open Field)

Scenario 2  (Cafeteria Setting)

Scenario 3
(L shaped lobby)

Planners

# possible actions in POMDP Planning: 4

# possible actions in POMDP Planning: 11

Experimental Details

  • For each scenario, we ran sets of 100 different experiments with different pedestrian densities in the environment.

# humans = 100

# humans = 200

# humans = 300

# humans = 400

Scenario 1

Results

Evaluation Metric: Travel Time (in s)

Results

Evaluation Metric: #Outperformed

Scenario 2

\(1D-A^*\)

\(2D-FMM\)

\(2D-PRM\)

Results

Limited Space Planner

Extended Space Planner

Conclusion

Thank You!

Himanshu Gupta

himanshu.gupta@colroado.edu

Extended Space POMDP Planning
(AAMAS 2022)

https://github.com/himanshugupta1009/extended_space_navigation_pomdp

Summary

 

  • We proposed a new technique for navigation problems with arbitrary state uncertainty by incorporating multi-query path planning techniques for effective tree search using online solvers for a POMDP
    • ​Probabilistic RoadMaps
    • Fast Marching Methods
       
  • We demonstrated that our technique outperforms the current state-of-the-art method significantly in terms of travel time while being just as safe!

Experimental Details

  • In simulations, the planning time for the vehicle at each step is 0.5 seconds

Experiments (NHV)

Limited Space

Planner 

Extended Space

Planners 

\(1D\)-\(A^*\)

\(2D\)-\(NHV\)

Results (NHV)

Evaluation Metric: Travel Time (in s)

Evaluation Metric: #Outperformed

Results (NHV)

\(1D-A^*\)

\(2D-NHV\)

Results (NHV)

AAMAS Conference Presentation

By Himanshu Gupta

AAMAS Conference Presentation

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