Autonomous MoCap systems, like AirCap, rely on robots with on-board cameras that can localize and navigate autonomously. More importantly, these robots must detect, track and follow the subject (human or animal) in real time. Thus, a key component of such a system is motion planning and control of multiple robots that ensures optimal perception of the subject while obeying other constraints, e.g., inter-robot and static obstacle collision avoidance.
Our approach to this formation control problem is based on model predictive control (MPC). An important challenge is to handle collision avoidance as the constraint itself is non-convex and leads to local minima that are not easily identifiable. A possible approach is to treat it as a separate planning module that modifies the MPC-generated optimization trajectory using potential fields. This leads to sub-optimal trajectories and field local minima. In our work [ ] we provide a holistic solution to this problem. Instead of directly using repulsive potential field functions to avoid obstacles, we replace them by their exact value at every iteration of the MPC and treat them as external input forces in the system dynamics. Thus, the problem remains convex at every time step. As long as a feasible solution exists for the optimization, obstacle avoidance is guaranteed. Even though field local minima issues remain, they become easier to identify and resolve. To this end, we propose and validate multiple strategies.
In ongoing work we address the complete problem of perception-driven formation control of multiple aerial robots for tracking a human using multiple aerial vehicles. For this, a decentralized convex MPC is developed that generates collision free formation motion plans while minimizing the jointly estimated uncertainty in the tracked person's position estimate. This estimation is performed using a cooperative approach [ ] similar to the one developed in our recent work [ ]. We validated the real-time efficacy of the proposed algorithm through several field experiments (see image above) with 3 self-designed octocopters and simulation experiments in a realistic outdoor environmental setting with up to 16 robots.